Tax Forecasting Methodological Review 2019

Department of Finance | Tax Forecasting Methodological Review 2019 Page | 1

Tax Forecasting Methodological Review 2019

December 2019

Department of Finance | Tax Forecasting Methodological Review 2019 Page | i

Executive Summary

This is the third methodological review of the Department of Finance’s approach to tax forecasting.

This Report assessed the accuracy of tax forecasts over the past decade. The overall forecasting

performance was found to be robust with the exception of the financial crisis years (2008 and 2009)

when errors were particularly large for all of the main tax heads. In more recent years, forecasts from

the Department have tended to err on the low side, pointing to a degree of prudence within the numbers.

Much of the over–performance in taxes has been driven by unexpectedly strong corporation tax receipts

since 2014. In general, forecasts were noticeably less accurate as the time horizon extends. That said,

the overall forecasting approach within the Department is in line with the approach taken by domestic

and international institutions such as the Central Bank of Ireland and the European Commission.

The performance of the four main tax heads – income tax, corporation tax (CT), value added tax (VAT)

and excise duties, which combined accounted for 92 per cent of the tax take in 2018, were assessed in

detail. The findings for both VAT and income tax pointed to relatively small forecasting errors. In

contrast, CT related errors were much larger. The difficulties inherent in forecasting CT have been well

documented but have been exacerbated in recent years as these receipts have grown in size.

A battery of more formal statistical tests and backcasting exercises were also carried out. On the whole,

these tests confirmed that the Department’s tax forecasts were both unbiased and also outperformed

alternative approaches. However, the weak performance of excise and CT forecasts were again

highlighted.

A number of recommendations are included in the report. These include the need to refine forecasts

for VAT, CT and excises. For VAT, the forecasts should be supplemented with a housing-specific

component. For CT, there appears to be merit in moving to a higher elasticity. Work is underway within

the Department in both of these areas. For excise, a more disaggregated approach is warranted bearing

in mind the diverse nature of the tax. The Group also noted and welcomed recent research in relation

to income tax elasticities, which have been incorporated into the forecasting methodology.

In light of the increasing prominence of general government aggregates, the Group also recommends

that the Department continues to develop its general government forecasting capacity alongside the

Exchequer tax forecasts considered in this report.

Department of Finance | Tax Forecasting Methodological Review 2019 Page | ii

Contents

Page

Executive Summary Tables, Figures, Boxes and Annexes

i

ii

1. Introduction 5

1.1 Background 5 1.2 Terms of reference 5

1.3 Group membership 5

1.4 Main findings of the 2008 Report 5

1.5 Follow up on the recommendations of the 2008 Report 6

1.6 Structure of the Report 6

2. Composition of Irish taxes in 2018 7

2.1 Trends in exchequer taxes 7 2.2 Macroeconomic context 11

3. Forecasting methodology and accuracy 14

3.1 Tax forecasting methodology 14 3.2 Approaches to forecasting in other agencies 16 3.2.1 The Central Bank of Ireland’s approach 16 3.2.2 The European Commission’s approach 17

3.2.3 Summary of the approaches 18 3.3 Forecasting accuracy 19 3.4 Current and year-ahead performance 20

3.4.1 Current year estimates 20

3.4.2 Year-ahead forecasts 20 3.5 Forecasts for the main tax heads 22 3.5.1 Income tax 22

3.5.2 VAT 25

3.5.3 Corporation tax 26 3.5.4 Excise duties 27 3.6 Smaller tax heads – CGT, CAT, stamps and customs duties 28

3.6.1 Capital taxes 28

3.6.2 Customs duties 30

4. Detailed forecast errors appraisal 32

4.1 Introduction 32

4.2 Forecast bias and benchmarking 32

4.2.1 Methodology 32

4.2.2 Results 33 4.3 A decomposition of one-year ahead forecast errors (2008-18) 34

4.4 Forecasts on a general government basis 39

4.4.1 Accuracy of forecasts 40

4.4.2 Benchmarking general government forecasts 41

5. Tax revenue elasticities 43

5.1 Introduction 43

Department of Finance | Tax Forecasting Methodological Review 2019 Page | iii

5.2 Elasticity concepts and recent research 43 5.3 Historical tax to output elasticities 44

5.4 Elasticity for the main tax heads 45

5.4.1 Income tax 46

5.4.2 VAT 47 5.4.3 Corporation tax 48

5.5 Lessons from quarterly data 48

6. Forecasting Recommendations 51

6.1 Introduction 51 6.2 Income Tax and USC 51

6.3 VAT 51

6.4 Corporation tax 52

6.5 Excise duties 53 6.6 Other taxes 53

6.7 Other recommendations 54

6.8 Summary of recommendations 54

7. Conclusion 56

Tables, Figures, Boxes and Annexes

Tables

Table 1 Recommendations from 2008 Report 6 Table 2 Exchequer tax take at end-year 7 Table 3 Current year (t) forecast errors 20 Table 4 Year ahead (t+1) forecast errors 21

Table 5 Capital and other taxes, selected years 28 Table 6 Forecast bias check 33

Table 7 Benchmark check – budget forecasts relative to a random walk 33

Table 8 Summary of forecast error decomposition 36

Table 9 General government forecast errors 41 Table 10 Income tax elasticities 47

Figures

Figure 1 Taxes in 2007 and 2018 8

Figure 2 Annualised exchequer taxes, 2007 to mid-2019 9 Figure 3 The big 4 taxes 9 Figure 4 Selected macroeconomic drivers 10 Figure 5 Macroeconomic developments 12 Figure 6 Flow-chart of forecasting approach 14 Figure 7 Income tax forecast errors 23 Figure 8 VAT forecast errors 25 Figure 9 CT forecast errors 26 Figure 10 Excise forecast errors 27 Figure 11 CGT forecast errors 29 Figure 12 Stamp Duty forecast errors 29 Figure 13 CAT forecast errors 30

Department of Finance | Tax Forecasting Methodological Review 2019 Page | iv

Figure 14 Customs duties forecast errors 31 Figure 15 VAT forecast error decomposition 37 Figure 16 Income tax (PAYE) forecast error decomposition 37 Figure 17 Corporation tax forecast error decomposition .38 Figure 18 Excise duties forecast error decomposition 39 Figure 19 Taxes on a general government and exchequer basis 40 Figure 20 General Government tax forecast errors 41 Figure 21 General Government tax forecast errors- current year 42 Figure 22 General Government tax forecast errors- year ahead (t+1) 42 Figure 23 Aggregate tax to output elasticities 44 Figure 24 Tax to GNI* elasticities with a 95 per cent confidence interval 45 Figure 25 Income taxes and employee compensation 49 Figure 26 Corporation taxes and net operating surplus 49 Figure 27 VAT and nominal consumption 50 Figure 28 Excises and consumption of goods 50

Boxes

Box 1 A heat-map approach to assessing tax outturns 11

Box 2 The timing of budget forecasts 22

Box 3 Forecasting pay related social insurance- performance and approach 24

Box 4 Research on tax revenue elasticities 46

Annexes

Annex A Group membership and meetings 57

Annex B Forecast summary decomposition 58

Annex C Alternative forecasting approaches 59


Chapter 1 Introduction

1.1 Background

This is the third methodological review of tax forecasting undertaken by the Department of Finance

(herein the Department). This assessment was carried out by the Tax Forecasting Methodological

Review Group (TFMRG), a group of officials from within the Department and other organisations with a

particular expertise in taxes, economic forecasting and the public finances. The reviews are designed

to both assess forecasting accuracy and to make recommendation for improvements going forward.

The last report was published in 2008 (Department of Finance, 2008), and is detailed below.

1.2 Terms of Reference

For the 2019 review, the terms of reference tasked the Group with reviewing the existing tax forecasting

methodology and examining and quantifying tax forecast errors with a focus on the four largest tax

heads – income tax, corporation tax (CT), value added tax (VAT) and excise duties. The information

bases upon which forecasts are made was to be assessed looking at both relevant literature and

experiences from other jurisdictions. The Group was also tasked with making recommendations for

changes to the existing forecasting methodology, where appropriate. Finally, aside from the largest tax

heads, the Group was also required to consider some of the smaller taxes (capital taxes, stamp and

customs duties) as well as social contributions.1

1.3 Group Membership

The Group was comprised of officials from the Department of Finance and external bodies - the Central

Bank of Ireland, the Revenue Commissioners and the European Commission. The Department hosted

seven meetings between April and November. These meetings focused on specific tax heads -

specifically the composition of taxes, the approach and performance of forecasts and any

recommendations for change (for details see Annex A). Before proceeding it is worthwhile to briefly

review the findings of the 2008 report and the extent to which recommendations were followed.

1.4 Main Findings of the 2008 Report

The work underlying the 2008 tax forecasting review occurred at a time when the economy was

undergoing exceptionally strong growth, much of which was related to a property boom. In the run up

to the report, taxes had consistently outperformed budget day targets in large part due to housing

related transaction receipts. The latter showed up most clearly in stamp duties, capital taxes, VAT and

income tax receipts.2

The report focused on tax forecasts from 1999 to 2006. Over this period, taxes significantly diverged

from forecasts with an overall root mean square error (RMSE) of 6.1 per cent.3 The role of one-off

1 Receipts from social contributions are not forecast by the Department of Finance. 2 For a detailed discussion on the housing market and Irish taxation receipts see papers by Addison-Smyth and McQuinn (2010 and 2016). 3 The RMSE is a commonly used metric to assess forecast accuracy – for more details, see Chapter 3.


factors and forecast errors in macroeconomic variables in influencing the size of the RMSE were

highlighted. When controls were put in place for these factors, the forecasting accuracy improved with

a decline in the RMSE to 4.0 per cent. The Report noted a prudent bias in forecasts, with outturns

typically in excess of Budget day forecasts. The approach to forecasting was similar to methods

followed internationally, although errors in Ireland were reported as being on the high side.

All of the main tax heads were assessed for forecast accuracy, resulting in a number of points being

raised. These included:

Income taxes – a pattern of undershooting in forecasts linked to a series of key changes in the

tax system.

VAT – complement the existing approach (based on consumption patterns) with a separate

forecast for VAT related to housing.

CT – consider the use of Gross Operating Surplus (GOS) in the forecasting equation.

Stamp Duty – use a disaggregated approach including forecasts for construction related

(housing and non-housing) activity.

Overall the report recommended that a cautious approach be adopted for property related revenues.

Similarly, the overarching guide for tax forecasting should be to maintain an overall tax to GDP elasticity

of unity as a ‘top-down’ check of tax forecasts.

1.5 Follow up on Recommendations of the 2008 Report

A summary of the main recommendations and their implementation is shown in table 1.

Table 1: recommendations from 2008 report

Recommendation Result

Maintain an aggregate tax-to-GDP elasticity of 1.0. Maintained

Project VAT receipts from housing separately from other VAT receipts Implemented but not maintained

Use Gross Operating Surplus (GOS) as a driver of CT forecasts Implemented

Maintain the disaggregated approach to forecasting stamp duty Maintained

Maintain cautious approach to forecasting property-related tax revenue Maintained

Undertake more regular analysis of tax forecasting performance Since establishment in 2011, the Irish

Fiscal Advisory Council (IFAC) has been assessing forecasts.

Source: Report of the Tax Forecasting Methodology Review Group 2008.

1.6 Structure of the Report

This report is structured as follows. Chapter 2 examines the composition of taxes in Ireland and changes

in both taxes and the macroeconomic backdrop over the past decade. The Department’s tax forecasting

methodology and overall accuracy is set out in Chapter 3, before a more detailed assessment is carried

out in Chapter 4. Tax elasticities are discussed in Chapter 5 before recommendations are put forward

in Chapter 6. Chapter 7 concludes.


Chapter 2 Composition of Irish Taxes in 2018

2.1 Trends in Exchequer Taxes

The Exchequer tax take is summarised in table 2 below. In 2018, tax receipts amounted to €56 billion.

The 4 largest tax heads – income tax, VAT, corporation tax (CT) and excises accounted for 92 per cent

of the total. Since the last tax forecasting report, the composition and size of the tax base has changed

significantly (figure 1). Income tax and CT now account for a much larger share of overall tax revenue

with capital related taxes (capital gains tax (CGT), capital acquisitions tax (CAT) and stamp duties)

much smaller. Capital taxes had risen sharply prior to the last report due mainly to housing related

activity – peaking in 2006 at €7 billion (16 per cent of tax revenue), before declining abruptly to €1.5

billion in 2010. Since then, their share of the tax take has remained relatively stable at close to 5 per

cent.

Table 2: exchequer tax take at end-year, € billion

2005 2007 2010 2015

2016

2017 2018

Share of tax

in 2018

Custom duties 0.2 0.3 0.2 0.3 0.3 0.3 0.3 1

Excise duties 5.2 5.8 4.7 5.3 5.7 5.9 5.4 10

Capital gains Tax 2.0 3.1 0.3 0.7 0.8 0.8 1.0 2

Capital acquisitions Tax

0.2 0.4 0.2 0.4 0.4 0.5 0.5 1

Stamp duties 2.7 3.2 1.0 1.3 1.2 1.2 1.5 3

Income taxes 11.3 13.6 11.3 18.4 19.2 20.0 21.2 38

Corporation tax 5.5 6.4 3.9 6.9 7.4 8.2 10.4 19

VAT 12.1 14.5 10.1 11.9 12.4 13.3 14.2 26

Total 39.3 47.2 31.8 45.6 47.9 50.7 55.6 100

Source: Department of Finance.


Figure 1: taxes in 2007 and 2018, percentage share

Since 2008, motor tax for private cars is assessed on the basis of CO2 emissions.


In figure 2, annualised taxes are shown since 2007 with figure 3 indexing taxes to January 2008.4 These

charts highlight the marked fall and recovery in receipts as a result of the financial and economic crisis

in 2008/09. Taxes returned to pre-crisis peaks in 2016. The increasing prominence of the four largest

tax heads and in particular income tax and CT is notable. The latter two taxes have increased on an

average annual basis by 5.2 and 9.1 per cent, respectively over the past decade, outstripping the growth

rate of overall taxes (of 3.5 per cent).

Tax receipts have also been helped by the sharp recovery in underlying economic activity including

corporate profitability. Figure 4 shows the main macroeconomic drivers behind the largest tax receipts,

indexed to 2008. Aside from growth, a series of policy related changes that include the introduction of

the universal social charge (USC) have helped the tax take. A more detailed discussion of recent trends

in taxation is provided in the Department’s ‘Annual Taxation Report’ (Department of Finance, 2019a).

Taxation trends are also regularly monitored and reported on by the Department through the monthly

‘Fiscal Monitor’ publication and the use of heat maps (box 1).

4 Taxes are annualised (12 month rolling totals) to smooth the data and to counteract seasonal patterns.

Customs1%

Excises12%

Capital Gains

7%

Capital Acquisitions

1%

Stamps7%

Income29%

Corporation14%

VAT31%

Taxes in 2007

Total: €47bn

Customs1% Excises

10%

Capital Gains

2%

Capital Acquisitions

1%

Stamps2%

Income38%

Corporation19%

VAT26%

Motor Vehicle Duties

2%

Taxes in 2018

Total: €56bn


Figure 2: annualised exchequer taxes, 2007 to mid-2019, € billions


Figure 3: the big 4 taxes, January 2008 = 100


0

10

20

30

40

50

60

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

Income Taxes VAT Corporation Tax Excise DutiesCustoms Stamp Duties Capital Gains Tax Capital Acq Tax

50

75

100

125

150

175

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

20

19

Income Taxes VAT Corporation Tax Excise Duties Total


Figure 4: selected macroeconomic drivers, 2008 = 100

Note: wages are proxied by the non-agricultural wage bill. PCE refers to personal consumption expenditure. GOS and NOS measure the gross and net operating surplus, respectively. All series are in nominal terms. Source: Department of Finance.

50

75

100

125

150

175

200

2252

008

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

GDP GNP GNI*

GOS Wages NOS

PCE


Box 1: a heat-map approach to assessing tax outturns Heat maps are a commonly used method of depicting a wide range of data in a convenient manner. The Department has

previously published heat maps for both macroeconomic and fiscal data.5 Following the same methodology, an exchequer

tax heat map is shown in the figure below based on an extended sample from 2005.6 For each of the tax heads, standardised

year-on-year growth rates are calculated. The shadings are based on the value of each tax in a particular quarter relative to

its mean. A growth rate of two standard deviations or more above the mean is assigned the darkest red; observations within

a standard deviation of the mean have a neutral shading. By highlighting deviations, the maps provide a fast high-level

overview of potential (or emerging) imbalances. It is important to stress that a variable ‘flashing red’ is not necessarily

problematic, although persistent strong colours may be symptomatic of emerging imbalances.

From the heat map, three distinctive phases stand out. First, the persistently strong growth rates across most of the tax

heads (and notably capital and transaction based taxes) in the lead-up to the financial crisis. Second, the comprehensive

and sustained collapse in taxes from 2008. It is also notable that receipts remained subdued for a prolonged period up until

mid-2011. Third, the broad-based recovery in taxes in recent years including robust CT receipts.

Figure: fiscal heat map for exchequer taxes, 2006Q1 to 2019Q3


2.2 Macroeconomic Context

The economic backdrop is central to the changes in tax yields illustrated above, as the economy has

undergone a number of structural changes, encompassing a sharp contraction in activity and a

subsequent strong recovery. The economy is now two thirds larger than in 2007 (as measured on a

gross value added basis), with growth considerably more balanced across the sectors.

As evident in Figure 5 below, the labour market continues to perform strongly. Employment levels hit

new peaks in 2018, with more people in work than ever before. In 2007, approximately 2.2 million people

were employed, with 1 in 9 jobs in the construction sector and the services industry accounting for 70

per cent of total employment. In 2018, just 1 in 16 of the nearly 2.3 million people in work were employed

in construction while the services sector accounted for 76 per cent of total employment. Unemployment

continues to fall, with the unemployment rate now close to that seen in 2007. Furthermore, there is

5 For more details, see ‘Box 6 Fiscal Heat Maps’ 2019 Stability Programme Update (Department of Finance, 2019b) 6 The heat map above is based on year-on-year growth rates and therefore starts from Q1 2006.

https://assets.gov.ie/8305/88ffede238074f2cb88fc996854a12b3.pdf


evidence of a pick-up in wage growth, in the first half of 2019 in particular, in line with a tightening labour

market.

While initially slow to recover following the crisis, since 2014 private consumption has grown at a robust

rate. Consumption growth still remains below the rates seen between 2005 and 2007, however.

Figure 5: macroeconomic developments

Source: Central Statistics Office, Department of Finance

At the time of the last report, the booming construction sector resulted in very strong growth in VAT and

capital related taxes. Activity levels within the construction sectors are currently much lower, with

approximately 18,000 new homes constructed in 2018, behind estimated demand of 30-35,000 units

required per annum, and significantly lower than the 78,000 homes built in 2007 thus creating

consequent knock-on effects for capital related taxes. Another key difference relates to the corporate

sector, with the increasingly globalised Irish economy experiencing exceptional rates of increase in

corporation taxes in recent years.

While a return to a fiscal surplus was observed in 2018, the level of public indebtedness (at 104 per

cent of GNI*) remains high by both historical and international standards.7 In total, the stock of debt

amounted to €206 billion last year or €42,500 on a per capita basis, one of the largest in the OECD.

Aside from the stock of debt, the annual interest bill continues to represent a significant operating cost

for the State, amounting to €5.2 billion in 2018.

Since 2007 and the subsequent economic and financial crisis, several measures have been taken to

broaden the tax base. The universal social charge (USC) was introduced in Budget 2011 and was

7 Department of Finance Annual Report on Public Debt in Ireland 2019. Available at: https://www.gov.ie/en/publication/d45694-annual-report-on-public-debt-in-ireland-2019/

-250

-200

-150

-100

-50

0

50

100

150

19

99

Q1

20

00

Q3

20

02

Q1

20

03

Q3

20

05

Q1

20

06

Q3

20

08

Q1

20

09

Q3

20

11

Q1

20

12

Q3

20

14

Q1

20

15

Q3

20

17

Q1

20

18

Q3

'00

0s

Employment: Annual Change in Full-time and Part-Time

Full Time

Part Time

55

60

65

70

75

80

85

90

95

100

105

19

95

19

97

19

99

20

01

20

03

20

05

20

07

20

09

20

11

20

13

20

15

20

17

20

07

= 1

00

Real consumption per capita

https://www.gov.ie/en/publication/d45694-annual-report-on-public-debt-in-ireland-2019/


followed by a household charge in 2012 and the local property tax in 2013, while a carbon tax, was

introduced in 2010. At the same time as these tax increases, tax expenditures have been curtailed (e.g.

lapsed property reliefs).8

From a tax forecasting perspective, greater data availability and improvements in modelling capabilities

in the decade following the crisis, have increased the scope for wider and more detailed forecasts.

Despite this, forecasting the macroeconomic developments that underpin tax forecasts has become

increasingly challenging, given the growth in the economy in recent years. This is illustrated by

distortions to traditional measures of economic activity including GDP and the impact of globalisation

and capital mobility. As one of the most globally-integrated economies in the world, and with a large

foreign-owned component, interpreting conventional measures of economic activity is especially

challenging in an Irish context. Following the exceptional growth in GDP in 2015, the Central Statistics

Office (CSO) published an alternative measure of the size of the economy, so-called ‘modified gross

national income’ (denoted as GNI*). These changes and some of the implications are discussed in an

explanatory note by the Department (Department of Finance, 2018).

8 Further information on tax expenditures are available in the Department’s Annual Report on Tax Expenditures

available at: http://budget.gov.ie/Budgets/2020/Documents/Budget/Report%20on%20Tax%20Expenditures%20Incorporating%20the%20Outcomes%20of%20Certain%20Tax%20Expenditure%20and%20Tax%20Related%20Reviews%20completed%20since%20c.pdf. Further information on post-crisis base broadening measures can be found here: https://assets.gov.ie/5749/170119165651-e89ccd5500de476784176193cb6bef4c.pdf

http://budget.gov.ie/Budgets/2020/Documents/Budget/Report%20on%20Tax%20Expenditures%20Incorporating%20the%20Outcomes%20of%20Certain%20Tax%20Expenditure%20and%20Tax%20Related%20Reviews%20completed%20since%20c.pdf




Chapter 3 Forecasting Methodology and Accuracy

This Chapter examines the methodology used in tax forecasting before an overall assessment is made

of accuracy, with a specific focus on the past decade.

3.1 Tax Forecasting Methodology

Forecasts for taxes are typically derived using a range of equations that link taxes to underlying

macroeconomic drivers. The latter can be a variable (or variables) that are known to be highly relevant

for the tax in question. The standard forecasting equation is set out below (also summarised in figure

6):

Taxt+1 = (Taxt – Tt)*(d(Macrot+1)*E) + Tt+1 + Mt+1 + Jt+1, (1)

Taxt+1 = tax take in year t+1

Tt = temporary or one-off factors affecting the tax take in the current year

Tt+1 = temporary/one-off factors affecting the tax take in the year t+1

d(Macrot+1) = growth rate in the macro driver (the proxy for growth in the tax base)

E = elasticity between the tax take and tax base

Mt+1 = effect of fiscal or budgetary measures impacting the tax take in year t+1

Jt+1 = judgement applied to alter the tax forecast for year t+1

Figure 6: flow-chart of forecasting approach

Source: Department of Finance

The elasticity is a key input as it shows how responsive each tax head is to the underlying driver. These

elasticities are revised periodically. While elasticities vary across time and by tax category, the overall

tax-to-GDP elasticity in Ireland has remained close to unity (see Chapter 5).

Budgetary measures consist of announced policy changes that will impact on the tax yield. These are

estimated by the Department and the Revenue Commissioners and are reported in the Budget on both

a current and full year basis. These include changes to tax rates, allowances, credits, exemptions and

tax bands. The Irish Fiscal Advisory Council published a working paper this year that includes a tax

policy measures dataset (Conroy 2019). This paper along with the IMF’s tax policy reform database,

Estimated

tax yield

(t)

+ (-) the effect of one-offs) affecting

the current year's base

Macro driver Elastcity

+ (-) the effect of one-offs) affecting the yield in year

t+1

+ (-) the effect of

Budgetary measures on yield in year t+1

+(-) Judgement in year t+1

Forecast tax yield

(t+1)


which uses the OECD surveys of changes in taxation (IMF, 2018), are important additions to the public

knowledge base.

In summary, the Department’s forecasting approach for the main tax heads relies on a range of inputs

on macroeconomics aggregates, elasticities, budgetary measures, temporary factors and judgement.

The latter is also informed by information provided by the Revenue Commissioners over the course of

the year in relation to the performance of each tax head.

The most aggregative forecasts are for VAT and CT. For the former, the growth rate in nominal personal

consumption expenditure is used with an elasticity of one. Following the 2008 report, the Department

adopted a more disaggregated approach using a combination of consumption and housing related

macroeconomic drivers, but the collapse in the property market negated this need. For CT, the

Department uses the growth rate in gross operating surplus (GOS), also with an elasticity of one. This

followed the recommendations of the 2008 report.

The forecasts for income taxes are disaggregated based on a range of separate forecasts for the main

subcomponents, namely, PAYE, self-assessed (schedule D) receipts, USC income (PAYE and self-

assessed) as well as other smaller items.9 The main sub-components are driven by projected changes

in labour market conditions as proxied by wage and employment growth, and adjusted by an elasticity.

The latter were re-estimated in 2017 (see Chapter 5). This resulted in the employment elasticity

dropping out of the equation and being replaced by a more comprehensive elasticity measure, reflective

of the wage distribution. Separate elasticities are used depending on the income tax sub-component.

Excises are disaggregated into two main sub-components – namely, excises excluding vehicle

registration tax (VRT) and VRT. The latter is estimated based on forecasts for the price and volume of

new car sales, with the former linked to growth in the volume of personal consumption expenditure.

Capital taxes (CGT, CAT and stamp duties) and customs duties are forecast along similar lines. CGT

and CAT are both linked to nominal GDP with customs driven by the projected change in the volume of

merchandise imports. In contrast, stamp duties are highly disaggregated with residential and non-

residential elements as well as non-housing and equity components. Each of these in turn is linked to

a specific macroeconomic driver – housing volumes and prices, other building and construction

investment, GDP (for equities) and consumer price inflation (for residual categories).

9 Other smaller components of income tax, include receipts from Deposit Interest Retention Tax (DIRT), Life Assurance Exit Tax (LAET), Relevant Contracts Tax (RCT), Professional Services Withholding Tax (PSWT) and Dividend Withholding Tax (DWT), back duty receipts and miscellaneous items are estimated separately with input from Revenue.


3.2 Approaches to Tax Forecasting in Other Agencies

3.2.1 The Central Bank of Ireland’s Approach

The Central Bank of Ireland (CBI) produces fiscal projections regularly throughout the year. The key

use for these is in the European Central Bank’s (ECB) Broad Macroeconomic Projection Exercise

(BMPE). These forecasts are published twice a year – in June and December – with interim projections

produced by ECB staff in March and September. The BMPE is a key input for the Governing Council in

its monetary policy decision making process. The BMPE includes short- and medium-term projections

(t+2 in June and t+3 in December) for the euro area and individual euro area countries for a wide range

of variables, including prices, real output, labour market and fiscal variables. It is a bottom up forecasting

exercise; national Central Banks (NCBs) produce individual country projections and these are

aggregated to generate a region wide outlook.10 The use of peer reviews and centralised assumptions

ensures a consistent approach across countries. The CBI’s fiscal projections are also used internally

as an input for Quarterly Bulletin forecasts and, more broadly, to support the CBI’s mission of

safeguarding monetary and financial stability.

With regard to tax forecasting, the methodology adopted by the CBI is broadly similar to that of both the

Department of Finance and the European Commission. Growth in individual tax heads are driven by

five main factors: (i) developments in an underlying macroeconomic base which drives the tax head, (ii)

the elasticity of the tax head to changes in this base, (iii) fiscal measures announced by the Government,

(iv) once off factors and (v) expert judgement. The latter is applied to take account of intra-year

information such as Exchequer returns, with projections over the medium-term more model based. The

macroeconomic variables which drive the fiscal projections are prepared by the CBI.

One key difference with the Department is that projections are made on a general government rather

than exchequer basis, with the focus on direct, indirect and capital taxes. Nevertheless, most individual

tax heads – such as income tax, corporation tax and VAT - are produced, both to support the

macroeconomic projections and to help build the broader tax components. Overall, broadly similar

macroeconomic bases and elasticities are used to those outlined by the Department of Finance and the

Commission. Focusing on the largest tax heads, changes to income tax are driven by a combination of

developments in non-agricultural employment and compensation per employee. CT is driven by gross

operating surplus, although with receipts decoupling from macroeconomic developments in recent

years a significant amount of judgement has been required. In the case of indirect taxes, the only sub

component projected is VAT, which is driven by developments in nominal private consumption.

A further difference is the role that peer reviews play in the forecasting process. To ensure that they are

realistic and prudent, each NCB projection is formally peer reviewed by both an ECB country expert

10 For a more detailed look at the Broad Macroeconomic Projection Exercise see ‘A guide to the Eurosystem / ECB staff macroeconomic projection exercises’, European Central Bank, July 2016.


and another NCB. One tool used to aid this process is the Disaggregated Framework (DF).11 While the

DF is primarily used to identify the structural path of the general government balance and the main

expenditure and revenue categories, it can also be used to examine the impact of important factors on

both the structural and unadjusted balances. On the revenue side, changes in unadjusted ratios of taxes

and social contributions are broken down into the following factors: (i) the fiscal drag, (ii) the decoupling

of the tax base from GDP, (iii) discretionary fiscal policy measures of a permanent nature and (iv)

residual developments. The first two factors show the impact of macroeconomic developments, the third

identifies the impact of macroeconomic policy and the fourth captures the effects of other, mostly

country specific factors that need to be explained on a case by case basis.12 These residuals occur

because the underlying tax model can only be an approximation of actual developments. In the Irish

case, for example, high residuals would appear on CT in the period 2015 to 2018, because the

underlying base used to project them – gross operating surplus – was not a good driver of receipts over

the period in question. Having to explain residuals – particularly positive ones which imply the tax ratio

is higher than the underlying model would imply – plays an important role in checking the consistency

of forecasts.

3.2.2 The European Commission’s Approach

The European Commission undertakes regular tax forecasts as part of the cross country projection

exercises.13 The forecasts are prepared by “country desk” experts within the Directorate General for

Economic and Financial Affairs (DG ECFIN). The exercises are conducted on a no-policy change (NPC)

basis.14 A three-step process is followed:

(i) Building a trend by extrapolating past revenue and expenditure trends and relationships.

When the revenue/expenditure items have a well-established link with some

(macroeconomic) aggregates, the forecast uses these relationships, i.e. elasticities with

respect to macroeconomic variables. For items that are not clearly correlated to other

aggregates, the method suggests extrapolation of past behaviour. The latter can take the

form of using rules of thumb, such as past ratios and/or applying the average growth rate

over an appropriate reference period.

(ii) Specifying the working assumptions that complement the trend projections and determining

the NPC baseline. This is useful, when time series contain structural breaks, specific multi-

year patterns observed in the past that are deemed likely to recur or when available monthly

indicators point to a deviation from an established long-term trend (for the in-year forecast).

11 For more details on the Disaggregated Framework see ‘A disaggregated framework for the analysis of structural developments in public finances’, ECB Working Paper Series, No.579 January 2006. A revised Disaggregated Framework was introduced this year by the ECB, although this primarily relates to structural rather than unadjusted developments. For more on the new methodology see ‘The new ESCB methodology for the calculation of cyclically adjusted budget balances: an application to the Portuguese case’, Braz et al, April 2019. 12 The fiscal drag captures the increase in average tax rates in a progressive income tax regime where changes to

tax bands do not keep pace with nominal income increases. The decoupling of the tax base from GDP can occur when growth in the tax base deviates from the growth rate of nominal GDP. 13 The forecast exercises take place four times a year, however the fully fledged forecasts, including fiscal, are bi-annual in spring and the autumn. 14 European Commission (2016), Report on Public Finances in EMU 2016, Institutional Paper 045, December 2016, Part II, Section 1, available at https://ec.europa.eu/info/sites/info/files/ip045_en_0.pdf

https://ec.europa.eu/info/sites/info/files/ip045_en_0.pdf


(iii) Incorporating the fiscal policy measures which are discretionary items that have been

credibly announced and are known in sufficient detail. This includes one-off measures.15

Fiscal forecasts by the Commission will typically extrapolate taxes forward in a way that is consistent

with past behaviour whilst accounting for measures that imply a change to any of these orientations on

the condition that they are sufficiently well detailed, or are either credibly announced or already adopted.

Forecasts include a limited number of working assumptions, especially to deal with possible structural

breaks.

The Commission’s fiscal forecast for Ireland closely mirrors this overall approach. Tax forecasts are

compiled using a bottom-up approach based on general government and national accounts data, as

this is the basis for multilateral fiscal surveillance undertaken by the Commission, i.e. the preventive

and corrective arms of the Stability and Growth Pact.16 As regards revenue items that are correlated

with macroeconomic variables, their trend is linked to the projections of these variables, which are

prepared by the Irish country desk in DG ECFIN within the same forecast exercise. Forecasts of the

main tax revenue aggregates (taxes on production and income on a general government basis) are

also compiled on a bottom-up basis and are then compared with various indicators, notably ex-ante

elasticities with respect to key macroeconomic aggregates - namely private consumption, nominal GDP

and compensation of employees (wages). The elasticities and discretionary budgetary announcements

are periodically revised based on the most recent information available at the time. The methodology

for the four main tax heads is briefly described below.

The Commission splits personal income tax into four sub-categories – PAYE, USC, Schedule D and

Other. For PAYE, USC and Other, the Commission use forecasts for wages and salaries as their main

macro driver whereas Schedule D is modelled based on potential GDP and inflation (HICP). For VAT,

a weighted macroeconomic driver is used based on household consumption expenditure, general

government intermediate consumption, and both household and general government gross fixed capital

formation. CT is modelled on gross operating surplus with an elasticity based on OECD estimates. A

highly disaggregated approach is used for excises with several of the sub-components estimated

separately, linked to imports and trends in consumption.

3.2.3 Summary of the Approaches

Overall, both the Central Bank and the Commission’s approaches to forecasting are similar to that of

the Department. The Department’s forecasts provide a more detailed breakdown of taxes, particularly

on an exchequer basis. All of the organisations rely heavily on exchequer based tax data as these are

timelier and more frequent than accruals based series. The difficulty in linking the exchequer data

15 One-off and temporary measures are defined by the Commission as government transactions that have a transitory budgetary effect that does not lead to a sustained change in the budgetary position. European Commission (2015), Report on Public Finances in EMU 2015, Institutional Paper 014, December 2015, Part II, Section 3, available at https://ec.europa.eu/info/sites/info/files/file_import/ip014_en_2.pdf 16 The approach used for Ireland is also similar to that used for other small open economies. During the meetings

the Commission presented on both approaches to tax forecasting in Ireland and in Denmark.


(particularly at a more granular level) to broader general government tax aggregates was noted as a

particular issue for tax forecasting in Ireland.

A report on macroeconomic and revenue forecasting by the Australian Treasury noted that most

countries performed tax forecasts on this “bottom-up” basis, with the approach followed in the US,

Canada and New Zealand (Australian Treasury 2012). Specifically, the Treasury noted the wide use of

such “mapping models” whereby revenue forecasts are prepared alongside macroeconomic forecasts.

The latter in turn is used to grow the economic base thereby ensuring a consistency between the

revenue and macroeconomic numbers.

3.3 Forecasting Accuracy

For this report, forecast errors were calculated on a current year (t) and year-ahead (t+1) basis. The

former measures in-year estimates, whereas the latter reflects forecasts for the following year.17 The

forecasts were taken from successive Budget day publications and compared with end-year exchequer

outturns.18 The main period for analysis consists of the decade since the last report, although longer

time series were also examined. The past decade is a rich period for analysis as it incorporates the

economic collapse in 2008/09, the subsequent recovery and very strong growth, including a surge in

CT receipts, since 2014.

For each tax head, the forecast error was calculated based on the following equation:

𝐸𝑡,𝑖 = 𝑇𝑎𝑥𝑜𝑢𝑡𝑡𝑢𝑟𝑛,𝑡,𝑖 − 𝑇𝑎𝑥𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑡,𝑖

(2)

where:

Et,i: difference between the outturn and forecast for a given time period (i).

The mean error (ME) is the average forecast error for a given period (equation 3). The sign of the ME

gives an indication of the direction of errors. A positive number indicates that forecasts erred on the low

side and vice versa.

𝑀𝐸 =1

𝑇∗ ∑ 𝐸𝑡,𝑖 (3)

One potential problem with the mean error relates to cases where positive and negative errors largely

offset, thereby resulting in a misleadingly low number. To counter this, two alternative measures of

forecast accuracy are used - the mean absolute error (MAE) and the root mean squared error (RMSE).

The former measures the average (absolute) error without considering the direction. The latter (shown

in equation 4) is a commonly used metric in evaluation exercises, with larger errors more heavily

penalised, which is important from a tax forecasting perspective.

𝑅𝑀𝑆𝐸 = √[1

𝑇∗ ∑ 𝐸𝑡,𝑖

2 ] (4)

17 For example, Budget 2017 is published in October 2016 - the current year error refers to the outturn for taxes in 2016 whereas the year-ahead forecast relates to 2017. 18 The supplementary budget which was published in April 2009 was not included in the analysis.


3.4 Current and Year-ahead Forecasting Performance

3.4.1 Current-year Estimates

Looking at the period since 2008, the mean error was positive (i.e. tax forecasts erred on the low side)

at 0.2 per cent with a RMSE of 1.5 per cent (table 3). In nominal amounts, (absolute) errors have

averaged close to €0.3 billion per annum over the past decade with a particularly large error in 2015

(€1 billion) related primarily to unexpectedly strong CT receipts (see below). Errors for income tax and

VAT were relatively low, particularly given their size in the tax take. The overall errors were also heavily

affected by the unexpected collapse in tax revenues during the crisis. If the years 2008 and 2009 are

excluded, the overall RMSE improves to 0.8.

Table 3: current year (t) forecast errors, per cent

ME 2008-18

RMSE 2008-18

ME 2000-18

RMSE 2000-18

RMSE 2000-18 (excl crisis)

Custom duties -1.5 3.4 -0.9 3.2 3.3

Excise duties 0.6 2.4 0.3 1.9 1.8

CGT 8.5 18.1 1.8 16.7 15.5

CAT -2.1 14.8 -1.1 11.3 11.9

Stamp duties -1.6 4.0 -0.5 3.3 2.8

Income taxes 0.1 0.7 0.0 1.0 1.0

CT 0.5 7.5 0.3 5.8 4.2

VAT -0.3 0.8 -0.2 0.7 0.8

Total 0.2 1.5 0.1 1.2 0.8


3.4.2 Year-ahead Forecasts

Errors are larger for year-ahead forecasts with a much higher RMSE (table 4). Since 2008, there was

a negative mean error (i.e. forecasts were too high) but this was heavily affected by the crisis years and

2008 in particular. If 2008 and 2009 are excluded, the mean error turns positive, averaging €0.7 billion

since 2010. Looking at the extended period from 2000, the magnitude of the errors were similar, with a

RMSE of 6.4 per cent. The stand-out number is the double digit RMSE for CT, an error that has also

risen in size over the past decade. In nominal terms, the (absolute) error for overall taxes has averaged

€1.0 billion over the past decade, although this has been dominated recently by errors relating to CT.

Similar to the case above for current year errors, there is also a notable improvement in the RMSE (by

close to a third) if the years coinciding with the financial crisis (2008 and 2009) are excluded.


Table 4: year ahead (t+1) forecast errors, per cent

ME 2008-18

RMSE 2008-18

ME 2000-18

RMSE 2000-18

RMSE 2000-18 (excl crisis)

Custom duties -2.9 11.6 -2.3 16.1 16.0

Excise duties -1.0 4.2 -1.6 5.5 5.2

CGT -1.1 42.2 2.1 38.0 26.2

CAT -5.8 14.2 1.1 16.7 16.2

Stamp duties -4.4 25.5 0.7 22.8 16.3

Income taxes -1.4 2.7 -0.3 3.8 3.5

CT 5.2 17.3 2.2 13.7 12.2

VAT -2.4 5.7 -1.4 5.3 3.8

Total -0.9 6.7 0.0 6.4 4.6


One key development over the past decade that has influenced forecasting has been the change in the

date of the Budget from December to October (since 2013). This has complicated the assessment of

in-year tax estimates which in turn feed into forecasts for the following year. This is discussed in more

detail in box 2.


Box 2: the timing of budget forecasts A key development since the last TFMRG report has been the movement of the Budget from December to October since

2013, as part of reforms under the European semester. This change has had a number of implications for forecasting as the

dataset or base for taxes is more compressed. To get a sense of how timing matters in the chart below the share of taxes

received in post-Budget periods is plotted. Prior to 2014, the Budget was published in December which meant that tax

forecasts were compiled with 11 months of data for the current year – meaning that 94 per cent of the tax take was received.

In contrast, with the movement to October, forecasts are finalised with 9 months of data, meaning that just under a third of

tax revenues have yet to be received. Within this, income taxes account for a substantial share of receipts with close to a

third arising in the final quarter of the year. November is a particularly large month for (self-employed) income tax. Similarly,

receipts from CT and VAT are also sizable in the final quarter of the year.

Figure: share of taxes post-Budget, per cent of tax revenue

Source: Department of Finance calculations.

The movement of the Budget affects forecasts in a number of ways. First, estimates for the current year’s tax take are likely

to be less accurate with nine months of data. Second, forecasts for the following year (t+1) are directly affected by errors in

estimates for taxes in the current year. This is referred to as the ‘starting-point’ error (discussed in more detail in Chapter 4).

To investigate this issue, end-year tax returns were regressed on the tax take at end-September and end-November using

annual data from 1984, using an equation of the form:

𝐿𝑜𝑔(𝑡𝑎𝑥𝑡) = 𝛼 + 𝛽𝐿𝑜𝑔(𝑡𝑎𝑥𝑚=𝑗) + 𝜀

Where t refers to a given tax year, and m=j reflects the month chosen (receipts to September or November).

The resulting regressions were then used to perform in-sample forecasts from 2013 (see Table). The mean absolute error

for forecasts based on 11 months of data was lower, at 0.9 per cent, relative to 1.2 per cent for forecasts based on 9 months

of data. There was also a notable difference for 2018 with the end-November equation much closer to the annual outturn.

Table: tax outturns and estimates, € billions

2013 2014 2015 2016 2017 2018

Tax outturn 37.8 41.3 45.6 47.9 50.7 55.6

Estimate (data to end-September) 38.8 41.7 45.7 48.3 50.9 54.3

Error -1.0 -0.4 -0.1 -0.4 -0.2 1.3

Estimate (data to end-November) 37.5 40.7 44.8 47.8 50.5 55.0

Error 0.3 0.6 0.8 0.1 0.2 0.6


3.5 Forecasts for the Main Tax Heads

3.5.1 Income Tax

Income tax (IT) is the largest tax head and since the 2008 report, its share of revenue has increased

significantly, from 29 to 38 per cent. Receipts totalled €21.2 billion in 2018, up 6 per cent in the year.

0

5

10

15

20

25

30

35

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

CT VAT Income Tax Excise Other

October budgets


As an Exchequer heading, IT includes income tax, USC and DIRT. Within IT, PAYE income tax receipts

account for 70 per cent with USC accounting for close to a fifth. The balance is made up of self-assessed

income tax and other smaller items. The contribution from USC has flat lined in recent years as

economic growth balanced out policy changes.

Forecast errors for IT are plotted in figure 7 (and reported in the tables above). On average, errors are

relatively small with no clear evidence of any bias. In recent years, there has been a tendency for taxes

to slightly underperform relative to forecasts. The summary RMSE statistics for IT were 0.7 and 2.7 per

cent, on a current and year-ahead basis since 2008 with the accuracy of forecasts improving over the

period. In nominal amounts, one year-ahead (absolute) forecast errors have averaged €255 million over

the past decade.

Figure 7: income tax forecast errors, per cent


The Group’s discussions on IT focused on a range of factors, specifically the highly disaggregated

nature of the forecast and the split between PAYE and non-PAYE receipts and contributions from the

USC.19 Much of the debate centred on elasticities. The latter were updated in 2017 following research

conducted under the Department’s Joint Research Programme (JRP) with the ESRI, details of which

are included in chapter 5.20 The relatively strong forecasting performance was noted as was the

increasing reliance on income tax as a source of revenue. As highlighted in the Department’s Fiscal

Monitors, aside from income tax and USC, a significant amount of money received is in the form of pay

related social insurance (PRSI) each month. Although these are not forecast by the Department, they

are detailed in box 3, given their size and importance.

19 The USC was introduced (replacing the health and income levy) in 2011. In 2018, total PAYE income tax receipts amounted to €17.7 billion, with €3.2 billion accounted for by the USC. In addition, a further €2.3 billion was raised through self-assessed income tax which included €0.5 billion in USC. 20 https://www.esri.ie/current-research/joint-research-programme-on-the-macroeconomy-taxation-and-banking.

-8

-6

-4

-2

0

2

4

6

8

10

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

t

t+1

forecast too low

forecast too high


Box 3: forecasting pay related social insurance – performance and approach Overall Exchequer revenues amounted to €68.2 billion in 2018, of which €55.6 billion can be attributed to taxes. The

remainder is accounted for by a range of items, the biggest of which are PRSI receipts (€9.4 billion) which are used to finance

both the Social Insurance (SIF) and National Training Funds (NTF), although both funds include income from other sources.21

The latter includes income from investments, insurance awards, EU related monies and other miscellaneous items.

PRSI forecasts are compiled by the Department of Employment Affairs and Social Protection (DEASP). Given their size, this

box documents how these forecasts are prepared. Up until 2013, PRSI receipts were forecast by individual fund stakeholder

departments. This included the Departments of health, social protection, education, etc. Since 2013, the DEASP has taken

ownership of these forecasts. The approach to forecasting PRSI closely mirrors that for wider taxes. At budget time, an

estimate of the current year’s expected outturn is compiled based on data available at end-September. This estimate is

largely based on trends in the current year relative to the previous year but also reflects any changes in the macroeconomic

outlook (relative to the last set of estimates). For year-ahead forecasts, the estimated outturn in the current year is driven by

wage and employment macros provided by the Department of Finance. This number is then adjusted first, for the impact of

any previously announced budget measures (as is done for taxes) and second, to reflect any new measures announced in

the current budget.

PRSI forecasts are sub-divided into separate Schedule E (PAYE) and Schedule D receipts. Schedule E receipts (employer,

employee and self-employed directors) are also apportioned between the Social Insurance and National Training Funds

based on an analysis of receipts. For Schedule D (self-assessment from self-employed sole traders), forecasts are more

problematic due to the timing of receipts with close to 70 per cent received in November, with a further 13 per cent between

October and December. PRSI estimates and forecasts are finalised at the start of October once September receipts are

confirmed. Since 2013, PRSI outturns have exceeded forecasts on average by 2.8 per cent (€257 million), relative to initial

estimates. In general, these numbers tend not to be revised for the annual revised estimates for the public services volume

(REV).22

In recent years, PRSI receipts have tended to rise faster than overall employment and wages. This can be attributed to a

number of factors including a greater number of public service employees paying PRSI at a higher rate; proportionally more

employers paying contributions at the full class A rate; thresholds for PRSI not moving in sync with wages and changes in

the distribution of payers across different wage and salary deciles. Prior to 2013, forecast errors were considerably higher

mainly reflecting the effects of the economic and financial crisis. In 2009 and 2010 for example, PRSI receipts significantly

fell short of forecasts by close to 35 per cent (€2.5 billion) reflecting the unexpectedly large and severe shock to the labour

market. At the same time, in-year estimates proved too high and were significantly overstated in 2010 thus contributing to

sizable forecast errors. A further complicating issue arose from some employers incorrectly filing PRSI returns as USC during

2011. This resulted in social insurance fund income being overstated in 2011 before being offset in 2012.

Figure: PRSI forecast errors, per cent

Source: Department of Employment Affairs and Social Protection calculations.

21 PRSI receipts of €11.15 billion were received in 2018. The data published in the monthly fiscal monitor relates to government accounting reporting arrangements for PRSI. 22 Revised Estimates for Public Services 2019, available at: https://www.gov.ie/pdf/?file=https://assets.gov.ie/5035/201218100533-227c552da63d401ca85493a67c93e5de.pdf#page=1

-50

-40

-30

-20

-10

0

10

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

forecast too low

forecast too high

https://www.gov.ie/pdf/?file=https://assets.gov.ie/5035/201218100533-227c552da63d401ca85493a67c93e5de.pdf%23page=1

https://www.gov.ie/pdf/?file=https://assets.gov.ie/5035/201218100533-227c552da63d401ca85493a67c93e5de.pdf%23page=1


3.5.2 VAT

VAT has consistently been Ireland’s second largest source of tax revenue. In 2018, receipts amounted

to €14.2 billion – 26 per cent of Exchequer revenue. There are two main rates, the standard (at 23 per

cent) and a reduced rate (13.5 per cent). The standard rate applies to most goods and services although

certain categories are either exempt or liable at the reduced rate.23

The forecasting performance of VAT has been robust, particularly over the past decade, across a range

of metrics (figure 8). The RMSE statistics for current- and year-ahead forecasts were 0.8 and 5.7 per

cent, respectively, although the latter improves to 3.8 per cent if the crisis years are excluded. These

compare well with the other tax heads, repeating a pattern also evident in the 2008 TFMRG report. The

absolute size of one-year ahead forecast errors is similar in magnitude to IT at €289 million per annum.

Figure 8: VAT forecast errors, per cent


The Group’s main deliberations on VAT focused on data sources, the macroeconomic driver (nominal

personal consumption expenditure) and the role of the housing market. The Group discussed the extent

to which the existing macro driver fully captured online trades (and e-commerce) and VAT

developments. The Group noted that a number of items are included in the national accounts measure

of consumption that are zero-rated or exempt from VAT (e.g. food and rent). Ultimately, the Group

adjudged that the existing methodology was working satisfactorily although there appeared to be merit

in moving towards a more disaggregated approach, that better captures developments in the housing

market. This was also advocated in the 2008 report, though subsequently discontinued and is discussed

in more detail in chapter 6.

23 There are special reduced rates below the 13.5 per cent for a limited number of goods and services such as newspapers and electronically supplied publications (9 per cent) and agriculture (4.8 per cent). Most tourism related activities moved from the special reduced rate of 9 per cent to the 13.5 per cent rate following changes announced in Budget 2019.

-20

-15

-10

-5

0

5

10

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

t t+1 forecast too low

forecast too high


3.5.3 Corporation Tax

In 2018, Corporation tax (CT) amounted to €10.4 billion – 19 per cent of Exchequer revenue – increasing

by 27 per cent in the year. In recent years, CT receipts have surged – up 125 per cent since 2014.

Traditionally, CT has been a difficult tax to forecast – a fact compounded in recent years. In the 2008

report, CT had the largest error of the main tax heads. This was attributed to the size of the multinational

sector as well as a number of changes to the tax regime.24

Similar to the findings of the 2008 Report, forecast errors were largest for CT (bearing in mind the size

of the tax). In recent years, taxes have significantly overshot relative to forecasts with sizable errors

since 2014 (figure 9). In absolute terms, the one-year ahead errors are very high, averaging €739 million

per annum over the past decade, rising to €1.1 billion over the past 5 years. Overall this has meant that

close to three quarters of the total tax forecasting errors can be attributed to CT.

Figure 9: CT forecast errors, per cent


A number of points were raised during the Group’s discussions in relation to CT including the inherent

difficulty in forecasting such a tax. This is exacerbated in Ireland by the concentrated nature of receipts

and the marked rise in revenues since 2014 (when receipts increased by nearly 50 per cent). In respect

of the former, the large reliance on a relatively small number of companies and specifically the

contribution made by the top-10 companies is a particular issue.25

24 At the time of the 2008 report, there were a number of important changes to the corporation tax regime over this period, most notably the phased reduction to a standard 12½ per cent tax rate for trading income generally but also the decision to bring forward the payment date for preliminary corporation tax by 7 months, effectively to a current year payment basis. The 5 year transition period for the gradual move to a current year payment basis for corporation tax ended in 2006. The transition arrangements – whereby 1/5th of the amount due was brought forward in each year – generated cash-flow gains in each of the transitional years but from 2007 this cash-flow gain was lost. 25 McGuinness, G., and Smyth, D (2019), ‘Modelling recent developments in corporation tax’. Available at: https://www.gov.ie/en/publication/29cb12-modelling-recent-developments-in-corporation-tax/ Also various Revenue annual research papers on CT: https://www.revenue.ie/en/corporate/information-about-revenue/research/research-reports/corporation-tax-and-international.aspx.

-40

-30

-20

-10

0

10

20

30

40

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

t

t+1

forecast too low

forecast too high

https://www.gov.ie/en/publication/29cb12-modelling-recent-developments-in-corporation-tax/

https://www.revenue.ie/en/corporate/information-about-revenue/research/research-reports/corporation-tax-and-international.aspx

https://www.revenue.ie/en/corporate/information-about-revenue/research/research-reports/corporation-tax-and-international.aspx


Much of the work of the Group focused on the relationship between receipts and corporate profitability.

These links are not always clear-cut, particularly in real time. It was noted that in 2017 for example,

profitability remained relatively unchanged yet receipts increased by 12 per cent. One of the factors

behind this was a reduction in the repayment of the R&D tax credit among a small number of large

corporate tax payers. Recent developments in the international tax sphere including BEPS were also

noted.

In light of the size of forecast errors, the Group proposed testing to see if alternative elasticities or

macroeconomic drivers would improve accuracy (Chapter 4). The merits of changing the timing of CT

payments was also discussed, however it was agreed that a large administrative change like this would

need to lead to substantial benefits in terms of forecasting performance given the burden it would place

on companies. It was also noted that the impact of the change in the date of the Budget from December

to October has most likely contributed to forecast errors given that a high proportion of receipts are

received in the final quarter of the year (box 2 and chapter 4).

3.5.4 Excise Duties

Excise duties are arguably the most diverse tax with several rates applying to a multitude of products,

principally vehicles, alcohol, fuels and tobacco. In 2018, receipts amounted to €5.4 billion, down 9 per

cent in the year. The share of overall tax revenue has also fallen since the last report.

The forecasting performance of excises has been mixed. Over the past decade, the one-year ahead

absolute error has averaged €112 million. However, in 2018 the error was much larger (€402 million).

The 2018 error largely reflected a timing issue with tobacco taxes and the movement to plain packaging.

Figure 10: excise forecast errors, per cent


-20

-15

-10

-5

0

5

10

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

t t+1

forecast too high

forecast too low


The Group discussions focused on the multitude of sub-components under excises. Overall, the steady

fall in the share of taxes deriving from excises was noted (falling from 20 per cent in 1988 to 10 per cent

in 2018). Within excises, close to 80 per cent is accounted for by receipts from alcohol, tobacco and

fuels (excluding carbon). Changes in VRT over the economic cycle were also discussed. It was noted

that the recovery in VRT receipts (which accounts for 16 per cent of excises) and new car sales have

not reached pre-crisis levels. The significant shift towards diesel cars since 2008 was also discussed

and the likely impact of future switching of consumers towards electric and hybrid cars. These structural

changes could affect the base upon which forecasts are derived.

The Group discussed whether a more disaggregated forecasting approach was warranted going

forward given the range of subcomponents within excises and their susceptibility to policy changes. For

VRT, the merit in the existing approach was questioned, specifically the need for an exchange rate

channel given recent trends in car sales between Ireland and the UK.

3.6 Smaller Tax Heads – CGT, CAT, Stamps and Customs Duties

Aside from the four main tax heads, remaining taxes include capital gains tax (CGT), capital acquisitions

tax (CAT), stamp duty, customs duties and motor taxes. These taxes are diverse and relatively small.

That said, they are important sources of revenue and featured heavily in the recommendations arising

from the 2008 Report. At that time, capital taxes (CGT, CAT and stamp duties) accounted for over 14

per cent of the tax take, in large part due to the housing boom. In 2018 however, this share has fallen

to 5 per cent (see table 5). These taxes are briefly discussed below.

Table 5: capital and other taxes, selected years

2000 €bn

%

2007 €bn

%

2010 €bn %

2018 €bn

%

A. Custom duties 0.2 0.8 0.3 0.6 0.2 0.7 0.3 0.6

B. CGT 0.8 2.9 3.1 6.6 0.3 1.1 1.0 1.8

C. CAT 0.2 0.8 0.4 0.8 0.2 0.7 0.5 0.9

D. Stamp duties 1.1 4.1 3.2 6.7 1.0 3.0 1.5 2.6

E. Capital taxes (=B+C+D)

2.1 7.8 6.7 14.1 1.5 4.9 3.0 5.3

Other taxes 24.8 91.5 40.3 85.3 30.0 94.4 52.3 94.1

Total 27.1 100.0 47.2 100.0 31.8 100.0 55.6 100.0


3.6.1 Capital Taxes

Capital Gains Tax (CGT) is paid on the gains from the sale/gift or exchange of assets. Typically this

includes land, buildings and shares with different rates applying, the main one being 33 per cent. In

2018, receipts amounted to €1.0 billion (2 per cent of the tax take). Forecast errors for CGT are much

higher than for other tax heads. There has also been a clear tendency in recent years for forecasts to

err on the low side (figure 11). Despite their small weighting in overall taxes, absolute errors for CGT

were high averaging €112 million (the same as excises, despite being a fifth of the size) over the past

decade.


Figure 11: CGT forecast errors, per cent

Note: t+1 forecast error in 2008 peaked at 73 per cent (not shown).


Stamp duty is incurred on the transfer of property, equity and financial cards with several rates

applying.26 The share of stamps in overall receipts has declined sharply from a peak of €3.7 billion in

2006 (8 per cent of the tax take) to €1.5 billion (3 per cent of the tax take) last year. Forecast errors for

stamps were heavily affected by the period in the run-up to and following the financial crisis. Over the

past decade the absolute error has averaged close to €151 million – broadly similar in scale to CGT

and excises.

Figure 12: stamp duty forecast errors, per cent


26 In relation to stamp duty, it is important to note an annual pension levy (of 0.6 per cent) applied on the market value of assets in the years from 2011 to 2014.

-50

-25

0

25

50

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

t

t+1

forecast too low

forecast too high

-80

-60

-40

-20

0

20

40

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

t

t+1

forecast too low

forecast too high


Capital acquisitions tax (CAT) is payable on a gift or inheritance with at a rate of 33 per cent. In 2018,

receipts amounted to €0.5 billion or 1 per cent of the tax take – a share that has remained unchanged

over the past 2 decades. Absolute forecast errors on a one-year basis are small – averaging €29 million

over the past decade.

Figure 13: CAT forecast errors, per cent


The Group’s main discussions on capital taxes focused on the size of forecast errors, particularly for

CGT. It was noted that capital taxes in general are harder to forecast given the lack of suitable

macroeconomic drivers, specifically in relation to transactions. It was also highlighted that a relatively

small number of transactions could significantly influence receipts and thereby the size of forecast

errors. Timing was also an issue with the vast majority of payments (in the case of CGT) typically due

in December. The underperformance in stamp duty receipts was noted as was their relationship to

movements in equity and housing markets. The need for the current disaggregated approach to

forecasting stamp duty was queried, particularly given its size and whether a more streamlined

approach could be used.

3.6.2 Customs Duties

Customs duties are normally charged on the value of the item being brought into Ireland with a large

proportion collected and paid to the EU as part of Ireland’s annual budget contribution. Receipts from

customs duties have been broadly stable in recent years, totalling €333 million in 2018. The forecasting

performance has been mixed although in absolute terms, errors remain small.

While custom duties are currently only a small proportion of the tax take, the importance of this tax head

is likely to increase as a result of Brexit. Indeed at Budget time, the Department forecast custom duties

to more than triple between 2019 and 2020. This significant growth reflects the no-deal Brexit

assumption contained in the Budget 2020 forecasts. As pointed out in the Budget, this increase is purely

-50

-40

-30

-20

-10

0

10

20

30

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

t

t+1

forecast too low

forecast too high


technical in that the majority (80 per cent) of this figure will go towards an increased EU Budget

contribution.27

Figure 14: customs duties forecast errors, per cent


27 Budget 2020. Economic and Fiscal Outlook. Available at : http://budget.gov.ie/Budgets/2020/Documents/Budget/Budget%202020_Economic%20and%20Fiscal%20Outlook_B.pdf

-40

-30

-20

-10

0

10

20

30

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

t

t+1forecast too low

forecast too high

http://budget.gov.ie/Budgets/2020/Documents/Budget/Budget%202020_Economic%20and%20Fiscal%20Outlook_B.pdf

http://budget.gov.ie/Budgets/2020/Documents/Budget/Budget%202020_Economic%20and%20Fiscal%20Outlook_B.pdf


Chapter 4 Detailed Forecast Errors Appraisal

4.1 Introduction

This chapter takes a more detailed look at sources of forecast error with a particular focus on bias,

benchmarking and backcasting. Much of the analysis follows the methodology of previous reports and

more recent work in Ireland and the UK (Hannon, Leahy and O’Sullivan, (2015) and Bank of England

(2015)).

4.2 Forecast Bias and Benchmarking

4.2.1 Methodology

The unbiasedness of tax forecasts was assessed using a standard ordinary least squares (OLS)

regression involving the forecast error and a constant term with a null hypothesis that the latter is zero

(equation 5).

𝐸𝑡+𝑖 = 𝛼0 + 𝜇𝑡+𝑖 (5)

where 𝐸𝑡+𝑖 measures the forecast error in a given period (t and t+1).

The null hypothesis for unbiasedness is that 𝛼0 = 0. Values of 𝛼0 > 0 point to forecasts being too low

and similarly 𝛼0 < 0 point to forecasts being systematically too high.

An alternative check of the forecasts involved running benchmarking checks (common in forecasting

evaluation exercises). This involved comparing forecasts from successive budgets with those of a

random walk, as proxied by the year-end tax outturn.

More formally, the process can be described by the following equation:

𝑇𝑎𝑥𝑡 = 𝑇𝑎𝑥𝑡−1 + 𝜇𝑡 (6)

with E(𝜇𝑡) = 0.

The forecast errors from the random walk were then compared with budget day forecasts using a

Diebold-Marino test, with an equation of the form:

𝐷𝑖𝑓𝑓𝑡,𝑖 = 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝐸𝑟𝑟𝑜𝑟𝑡,𝑖2 − 𝑅𝑎𝑛𝑑𝑜𝑚 𝑊𝑎𝑙𝑘 𝐸𝑟𝑟𝑜𝑟𝑡,𝑖

2 (7)

where 𝐷𝑖𝑓𝑓𝑡,𝑖 refers to the difference between the squared forecast errors from the two approaches,

with ‘i’ distinguishing between current and one-year ahead periods. The next step then involved testing

to see the size, sign and significance of the difference, by regressing it on a constant term:

𝐷𝑖𝑓𝑓𝑡,𝑖 = 𝛼0 + 𝜇𝑡 (8)


The null hypothesis is that there is no difference between the forecasts (i.e. 𝛼0 = 0). In contrast, if 𝛼0 is

positive, then it can be asserted that the random walk process outperforms the budget forecast, and

vice versa.

4.2.2 Results

The bias and benchmark checks were done using regressions covering the period 1998 to 2018, with

a focus on both current and year- ahead forecast errors for the main tax heads. While the sample period

is small, the results are informative.28 Tests showed no evidence of any systematic bias over the

forecast horizon with the results summarised in table 6.

Table 6: forecast bias check

Current year bias (yes/no)

Constant term (t-statistic)

Year ahead bias (yes/no)


Income tax No 0.0

(0.8) No

-41.1 (-0.4)

VAT No -21.9 (-1.3)

No

-155.6 (-1.0)

Corporation tax No 47.9 (0.7)

No

218.5 (1.0)

Excise duties No 18.3 (1.2)

No

-77.0 (-1.4)

Total taxes No 54.5 (0.6)

No 79.8 (0.1)


For the benchmark tests, the forecasts significantly outperformed random walk series in most cases

(table 7).29 The only exception was for one-year ahead forecasts for excise duties. This finding coupled

with other metrics suggests that changes may be needed in relation to this tax head. Proposals are

outlined in Chapter 6.

Table 7: benchmark check – budget forecasts relative to a random walk

Current year outperforms benchmark

(yes/no)


Year-ahead outperforms benchmark

(yes/no)


Income tax Yes 68.1 (-4.0) Yes

-17.9 (-3.1)

VAT Yes -98.6 (-3.1) Yes

-70.6 (-2.9)

Corporation tax Yes -201.4 (-3.9) Yes

-47.1 (-2.0)

Excise duties Yes -37.8 (-3.3) No

-11.6 (-0.6)

Total taxes Yes -97.2 (-3.3)

Yes -58.3 (-3.0)


28 The estimations were carried out by ordinary least squares and also using HAC (Newey West) standard errors. 29 Forecast errors were compared with random walk errors, with the latter based on the outturn for the preceding year.


4.3 A Decomposition of One-year Ahead Forecast Errors (2008-2018)

There are many steps involved in generating a tax forecast. Before discussing recommendations, it is

important to get a fuller sense of errors through a more detailed forecast decomposition exercise. To

do this, each of the four tax heads were examined over the past decade. From Section 3.1, there are

several key inputs into a forecast. These include the estimate for the current year’s outturn, the forecast

for the macroeconomic driver (and the tax revenue elasticity), the assumed effect of budget measures

and judgement. Much of the analysis builds on the previous TFMRG report and work by Hannon et al.,

(op.cit,).30

Given this, four main sources of forecast error were assessed, namely:

starting point

macroeconomic

judgement

residual/other (which includes the tax revenue elasticity).

The components largely follow each of the main elements of the standard tax forecasting equation

outlined above and are discussed below. The tax revenue elasticity was not separately assessed as its

value in the forecasting equation does not change from year to year. If its value is an under/over-

estimate of the true parameter value, this will be picked up in the residual in this decomposition exercise.

A step-by-step backcasting exercise was followed whereby tax forecasts at the time of the budget were

taken and then retrospectively adjusted one at a time for a change in inputs.

The starting point error was assessed first. This is the proportion of the error caused by an incorrect

estimate for the current year’s tax take being used in the equation. In simple terms, the forecast for a

tax head in period t+1 is based on an assumed outturn for the tax in period t. Errors in the latter will

automatically feed through into the year-ahead forecast. To quantify this error, the correct tax outturns

were used in each budget day forecast (as opposed to estimates), leaving all other components of the

equation unchanged. This approach then highlights the proportion of the overall error accounted for by

an incorrect starting point.

The macroeconomic error was calculated in a similar fashion. Once again, the budget day forecast was

taken and adjusted solely for the latest observation of the macroeconomic driver, rather than the

estimate assumed at budget time.31 All other elements of the equation were kept unchanged. This then

helps to isolate the proportion of the error accounted for by the macroeconomic driver. This is typically

a large source of error.

30 The latter focused on tax forecasts over the period 2004 to 2014, finding that macroeconomic and other (residual) factors accounted for a substantial proportion of errors. 31 A further complication arises from the fact that macroeconomic aggregates themselves get revised (see Casey and Smyth, 2016). For the decomposition work here, the latest observed values of macroeconomic drivers were used.


To evaluate the extent to which judgement affected forecasts, the forecasts were amended to remove

adjustments imposed by the Department. Typically, these judgements involved either positive or

negative additions to the tax forecast at budget time to reflect additional information, not fully captured

in the macroeconomic driver. Following the approach above, all other elements of the budget day

equation were unchanged bar judgement to again isolate its impact.

Finally, other sources of error were captured by subtracting the three aforementioned errors from the

overall forecast error. This residual error is important as it acts as a “catch-all” variable, thereby

reflecting events or factors not accounted for by standard forecasting equations. Such factors could

allude to the elasticity being incorrectly estimated and/or the limits of a specific macroeconomic driver

in reflecting movements in the tax base.

A summary of each of these sources of error is provided in Table 8. Interpretation of the results is not

always intuitive as some of the factors can offset one another. For these reasons, the large forecast

error for CT in 2015 (an excess of €2.3 billion, 33.4 per cent) provides a useful example. Stripping this

down, the starting point component was small. The Department had assumed in the Budget that the CT

outturn in 2014 (the starting point for the forecast) would be €4.5 billion, whereas the actual outturn was

close to this at €4.6 billion. Similarly, there were very modest adjustments applied by the Department

to the forecast – these amounted to €35 million. These two factors alone therefore clearly indicate that

macroeconomic and other forecasting errors must have been at the heart of the €2.3 billion excess. At

the time the budget was prepared it was assumed that the macroeconomic driver (GOS) would increase

by 5.3 per cent in 2015. The actual outturn proved very different for well documented reasons with the

growth in GOS revised up to 60.0 per cent. This effectively captures most of the forecasting error. There

is also an important timing dimension to this type of retrospective analysis. If this decomposition

exercise had been replicated back in 2016, all of the error would have shown up in the residual, as the

CSO’s first estimate for the main macroeconomic aggregate in 2015 were much closer to the

Department’s forecast.32 However, in time, with data revisions, the macroeconomic driver was

substantially revised with the result that the error moved from the residual into the macroeconomic

component.

Overall, the findings are mixed as many of the errors tend to offset with at times no clear discernible

pattern. Looking at the past 5-years, macroeconomic factors have been a frequent and large source of

error, particularly for CT but also rather surprisingly for excises. Starting point errors have been a factor

for CT, VAT and excises although less important for income tax. Looking at judgements applied in

successive budgets, these tend to be relatively small. Perhaps the most notable finding from the

decomposition, is the sizable contribution from other/residual factors particularly for CT and excises

suggesting the need for modifications going forward. Each of the four main taxes are discussed in turn

below.

32 In 2016 for example, the CSO initially estimated nominal GDP and GNP growth rates for 2015 of 13.5 and 11.2 per cent, respectively. Most recently, these growth rates were estimated by the CSO at 34.9 and 22.9 per cent.


Table 8: summary of forecast error decomposition, per cent

Note: table measures one-year ahead forecast errors. Source: Department of Finance.

For VAT, the overall forecasting performance is strong with the main source of error arising from the

macroeconomic and other/residual components. In 2008 and 2009, the forecasts were too high primarily

as a result of macroeconomic forecasting error, with the residual operating in the same direction. This

reflects both the unprecedented fall in domestic demand in the economy at the time and the sharp

unwinding in the construction sector (which was a large source of VAT). In more recent years, the

residual component has been a factor but overall errors remain low. Looking at 2018, the

macroeconomic and other component captured much of the error, with consumption stronger than was

foreseen at budget time.

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Income Tax (PAYE)

starting point 0.5 -2.0 -0.4 1.5 0.0 0.4 0.1 -0.1 0.0 -0.1 0.0

macroeconomic -1.9 -15.5 -1.9 -3.3 -0.3 -1.1 -2.1 1.9 0.9 -0.7 -2.0

judgement 1.3 -3.9 -3.9 -5.1 -3.8 -0.6 0.0 1.0 0.6 1.2 0.3

other -1.4 2.3 5.9 6.0 6.9 0.6 5.8 -3.2 0.0 -0.3 3.2

Total -1.4 -19.1 -0.4 -0.9 2.9 -0.7 3.8 -0.4 1.4 0.0 1.6

VAT

starting point -1.1 -0.9 0.3 -0.7 0.1 -0.2 -0.3 0.7 -0.7 -1.7 -0.9

macroeconomic -5.0 -17.3 2.8 -1.5 0.2 -0.2 0.2 -0.1 0.3 -0.7 1.1

judgement 0.4 2.3 0.1 0.0 1.3 -0.5 -0.1 -1.4 -1.5 -0.5 -0.7

other -10.2 -14.9 -3.1 -3.1 0.2 -1.3 3.9 2.2 -1.7 2.4 1.5

Total -15.8 -30.8 0.1 -5.3 1.7 -2.2 3.7 1.4 -3.5 -0.5 1.0

CT

starting point 0.8 -23.9 2.9 4.4 -5.2 5.0 -1.9 1.4 10.8 -2.1 2.4

macroeconomic -4.8 -14.8 0.9 0.3 -0.7 -0.9 8.6 29.4 -3.0 4.2 3.7

judgement 1.3 13.8 4.3 0.9 9.7 -3.0 -0.2 0.3 -2.2 2.4 -1.0

other -29.6 -27.6 11.4 -19.8 6.7 2.0 -1.5 2.4 4.5 1.4 13.0

Total -32.3 -52.5 19.5 -14.2 10.6 3.2 5.1 33.4 10.0 5.9 18.1

Excises

starting point 0.5 -2.0 2.6 -2.3 -3.9 -1.5 0.1 -5.0 -2.2 -1.8 0.4

macroeconomic -5.4 -14.3 8.7 3.3 -0.4 -4.5 1.5 1.4 0.5 -1.9 1.6

judgement -4.7 -6.8 -7.2 -4.7 -2.8 -3.9 -2.8 -1.5 -1.1 -0.8 -4.9

other -0.4 1.5 -0.6 3.7 4.9 9.3 4.8 6.0 4.0 3.5 -4.4

Total -10.0 -21.6 3.5 0.1 -2.3 -0.6 3.6 0.9 1.2 -1.0 -7.4


Figure 15: VAT forecast error decomposition, per cent


For income tax, the table and figure below show decompositions for PAYE related income tax. This is

the main sub-component of income tax, although USC is discussed below. For PAYE, the forecasting

performance is reasonably robust. With the exception of the financial crisis years, errors have been

relatively small. One potential source of concern relates to the extent to which the residual component

largely offsets errors on the macroeconomic side. The Group felt that this could be reflective of the

wage side of the forecasting equation, where there are a number of sources of information on

compensation levels within the economy.

Figure 16: income tax (PAYE) forecast error decomposition, per cent


USC (PAYE) forecasts were also assessed looking at the period from 2012. The methodology

underlying USC forecasts mirrors that for overall income tax with separate PAYE and Schedule D

-40

-35

-30

-25

-20

-15

-10

-5

0

5

10

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

starting point macro driver other

judgement total error

forecast too low

forecast too high

-25

-20

-15

-10

-5

0

5

10

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018



forecast too low

forecast too high


components using both wages and employment as macroeconomic drivers. The main difference relates

to the (wage) elasticity measures which are lower for USC forecasts (1.2 for PAYE and 1.4 for Schedule

D). In contrast to most of the other tax heads, USC PAYE forecasts tended to err on the high side,

averaging -3.9 per cent. Much of this reflected a large undershoot in 2014 of close to 13 per cent. A

decomposition of the forecasts pointed to the residual as one of the main factors behind errors.

For CT, forecast errors are appreciably higher than for income tax or VAT. The main source of this

relates to the macroeconomic driver, confirming the difficulty in forecasting CT and limitations with the

existing equation. The large residuals in more recent years are difficult to account for but one potential

explanation could be linked to the tendency for macroeconomic aggregates to get revised (see Casey

and Smyth, op.cit,). This could mean that the published profitability figures (as encapsulated in gross

operating surplus) are not fully capturing activity levels in real time.

Figure 17: corporation tax forecast error decomposition, per cent


From the decomposition, there are three distinct phases with forecasts too high in 2008 and 2009,

reflecting macroeconomic forecasting errors linked to the turning point in the economy at the onset of

the global crisis and the housing market collapse. The latter two factors are also more likely to appear

in the residual series. From 2010 to 2014, CT forecasts performed reasonably well. The third phase

encompassing the period post 2014, however, is marked by much larger errors with outturns

significantly outperforming forecasts, notably in 2015 and 2018. This primarily reflects contributions

from macroeconomic aggregates, the residual and some larger starting point errors. The latter reflects

the fact that a large proportion of CT receipts are received post-Budget. The residuals in recent years,

as outlined above, could signal issues in respect of the macroeconomic outturns. It is clear that CT

remains a difficult tax to forecast, a point that was also made at the time of the 2008 report.

In this respect, recent research published by the Department in relation to CT modelling (McGuinness

and Smyth, 2019) is particularly timely. This work made use of error-correction models to generate one-

year ahead forecasts for in-sample comparison. Even with a range of macroeconomic drivers including

-75

-50

-25

0

25

50

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18



forecast too high

forecast too low


variants of both gross and net operating surplus, the models could not fully account for some of the

recent rises in CT. The research also noted the role of firm-level and international developments in

determining receipts. The paper highlighted that for current year estimates, the standard approach

adopted by the Department at budget time (which relies more on in-year trends and analysis from

Revenue survey data) worked well and outperformed a series of econometric models. This pointed to

the importance of specialist in-house knowledge within the Department and Revenue and the role of

judgement.

For excises, the effects of the financial crisis from 2008 are evident with the unexpectedly large and

persistent drop in consumer spending culminating in a large forecast error. More recently, outturns have

tended to exceed forecasts although the persistent nature of the residual – it has been positive in 6 of

the last 7 years - points to potential issues with the forecasting equation. In 2018, the large negative

error is accounted for judgment and the residual. The Group suggested that this was most likely

reflective of a carryover from the distorting impact of the tobacco plain packaging initiative. This resulted

in excise duties being persistently above profile through 2017 (as payments were frontloaded) and

below profile during 2018.

Figure 18: excise duties forecast error decomposition, per cent


A summary of the retrospective analysis is shown in Annex B. This analysis suggests that in the majority

of cases, judgement improved forecasting performance (errors were smaller when judgement was

included). The same finding applied for the use of the correct starting point for all taxes bar excise. For

macroeconomic aggregates, this was a source of error for CT, excise and VAT, although less so for

income tax. Overall, the Group was of the view that periodic forecast decompositions were useful while

recognising some of the limitations with such retrospective analysis.

4.4 Forecasts on a General Government Basis

While this report is primarily concerned with taxes on an Exchequer basis, the Group felt it useful to

also consider general government tax forecasts. These are broader than their exchequer counterparts

-30

-25

-20

-15

-10

-5

0

5

10

15

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

starting point macro driver

other judgement

total error

forecast too low

forecast too high


and feed directly into general government deficit and debt aggregates. General government data are

prepared in line with the European System of Accounts (ESA) 2010 methodology, with flows recorded

on an accruals basis. In simple terms, this means that flows are recorded when the related economic

activity takes place. A series of adjustments are required to convert the cash-based (exchequer) data

to accruals – particularly for taxes such as VAT, excises and income tax (PAYE).33

The main sub-components of general government revenue are taxes (74 per cent of the total) and social

contributions (16 per cent). Other smaller elements include interest, sales and transfer related income

accruing to government. In figure 19, taxes on an exchequer and general government basis are plotted.

In 2018, general government taxes reached a new peak of €61 billion.

Figure 19: taxes on a general government and exchequer basis, € millions


4.4.1 Accuracy of Forecasts

In figure 20, current and year- ahead forecast errors for general government taxes are shown for the

period 2002 to 2018. With the exception of the financial crisis, forecasts have consistently erred on the

low side – on just four occasions have receipts underperformed relative to forecasts (and in 2016/17

the differences were marginal). The mean errors for current and year ahead tax forecasts were 3.9 and

3.5 per cent respectively (table 9). On a RMSE basis, the respective errors were 4.9 and 8.8 per cent.

Over the past decade, the mean absolute error for taxes on a current year basis averaged €1.5 billion

and €2.5 billion in year-ahead terms. The accuracy of forecasts improves once the financial crisis years

are omitted (2008/09) with the RMSE falling to 7.0 and 7.4 per cent for overall revenues and taxes,

respectively.

An important development since the last tax forecasting report involved the setting up of the Irish Fiscal

Advisory Council (IFAC) in 2011. IFAC is an independent statutory body and as part of its mandate it is

required to formally assess the Department’s official forecasts, which includes budgetary and general

government forecasts. This involves IFAC publishing a bi-annual Fiscal Assessment reports. These

33 The ‘walk’ from the exchequer to the general government balance is documented in Annex 3 of Budget 2020.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

General Government

Exchequer


contain detailed analysis on the Department’s macroeconomic and budgetary forecasts. In addition,

IFAC has also published several analytical pieces on tax forecasting. This includes work on elasticities,

corporation tax, deposit interest retention tax as well as detailed analysis on tax forecasting errors.34

Table 9: general government forecast errors, per cent

Mean error (€mn)

Mean error RMSE RMSE

(excluding crisis years)

Total revenue - current-year (t) 1,897 3.0 4.0 4.1

Total revenue - year-ahead (t+1) 2,123 3.3 8.0 7.0

Total taxes - current-year (t) 1,622 3.9 4.9 4.7

Total taxes - year-ahead (t+1) 1,538 3.5 8.8 7.4

Note: sample period from 2002 to 2018.


Figure 20: general government tax forecast errors, per cent


4.4.2 Benchmarking General Government Forecasts

There are a relatively limited number of agencies that publish detailed general government forecasts to

compare with the Department. The European Commission, as set out above, produces regular

economic forecasts for Ireland as part of cross-country fiscal surveillance exercises. For benchmarking

purposes, the Commission’s autumn forecasts were used as these were closest in time to the

Department’s budget day numbers.

To proceed, forecasts for total revenue and tax receipts on a general government basis were compared

on a current and year-ahead basis. The results are summarised in figures 21 and 22. On average, the

forecasting performance was similar, with outturns typically exceeding forecasts and with errors notably

34 For working papers click on this link with analytical notes detailed here and fiscal assessment reports here.

-20

-15

-10

-5

0

5

10

15

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

t t+1

forecast too low

forecast too high

https://www.fiscalcouncil.ie/working-papers/

https://www.fiscalcouncil.ie/analytical-notes/

https://www.fiscalcouncil.ie/fiscal-assessment-reports/


higher on a one-year ahead basis. There also appears to have been a marked improvement in

forecasting accuracy over the past 5-years.

A final check involved testing to see if forecasts were statistically different from one another. This

involved regressing the differences in forecast errors between the two agencies on a constant term for

both overall revenue and taxes. The results, bearing in mind a relatively small sample, indicate that tax

forecasts were statistically different on both a current and year-ahead basis, although this did not hold

for overall government revenue.

Figure 21: general government tax forecast errors - current year, per cent


Figure 22: general government tax forecast errors - year ahead (t+1), per cent


-2

0

2

4

6

8

10

12

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

DOF COM

-20

-15

-10

-5

0

5

10

15

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

20

16

20

17

20

18

DOF COM


Chapter 5 Tax Revenue Elasticities

5.1 Introduction

From the standard tax forecasting equation in Chapter 3, the choice of the elasticity parameter is clearly

of significant importance. Historically, these parameters have averaged close to unity in Ireland,

although depending on the tax head in question the number can vary. Tax elasticities are periodically

reviewed and revised. This Chapter takes a more detailed look at elasticities and research in the area.

5.2 Elasticity Concepts and Recent Research

Before proceeding it is useful to distinguish between a tax revenue elasticity and buoyancy. The latter

is the observed change in revenues in response to changes in economic activity, while the elasticity is

the automatic change in revenues in response to changes in economic activity. Automatic changes

refer to revenue growth in the absence of discretionary policy changes. All other things being equal, if

there are no policy changes, then the two concepts coincide. Although not formally assessed in this

Report, a recent paper by the Parliamentary Budget Office (2019) estimated an average buoyancy for

Ireland of close to unity suggesting that taxes move in line with economic growth.

A tax revenue elasticity can be characterised as a structural elasticity, in the sense that it relates directly

to the design features of a given tax system. Another important form of elasticity is a behavioural

elasticity (which measures the extent to which economic decisions change in response to changes in

the tax rate). Generally speaking however, the Department and Revenue prepare forecasts on a static

basis, i.e. ignoring any behavioural change in response to the tax rate, such as changes in compliance

or economic activity. This is common practice across other countries and international agencies, given

the uncertainty inherent in predicting behavioural responses to tax.

The current exception to this approach relates to excise duties. Revenue apply behavioural elasticities

to petrol, diesel, alcohol, cigarettes and cars. These elasticities are based on empirical estimation of

consumer responses to changes in tax rates. They indicate, for example, strong consumer (taxpayer)

sensitivity to changes in the rate of duty applied to alcohol. If the forecast did not account for such a

behavioural response, future revenues would be mis-estimated.

Under the Joint Research Programme (JRP), the Department, Revenue and the ESRI recently

completed a study on behavioural elasticities for income tax (Department of Finance 2018b). The

parameter of interest, the elasticity of taxable income (ETI), measures how individual tax-payers adjust

their taxable income in response to changes in the amount of income which they retain after taxation

(known as the net-of-tax rate). The adjustment captures both labour supply and tax planning responses.

The research estimates a central ETI of 0.168, meaning that for each 1 per cent decrease in the

marginal net-of-tax rate, taxable income falls by 0.168 per cent on average. This lies in the bottom half

of the international range, which may be due to Ireland-specific factors such as a relatively smaller level


of tax deductions and reliefs here compared to elsewhere, a high degree of income tax compliance and

labour market frictions for PAYE earners in particular. The relatively low responsiveness compared to

other countries, coupled with the well-known progressivity of the Irish income tax system, suggest that

the trade-off involved in pursuing both equity and efficiency objectives in the Irish income tax system is

reasonably limited.

5.3 Historical Tax to Output Elasticities

In the two previous tax forecasting reports (in 1998 and 2008) long-run tax to GDP elasticities of 1.1

and 0.9 per cent were estimated. Neither of the equations controlled for policy changes over the period,

so strictly speaking they are indications of tax buoyancy. To update this analysis, using an extended

time series to 1970, tax elasticities were re-estimated using both GDP and adjusted Gross National

Income (GNI*).The results are summarised in figures 23 and 24, showing elasticities (including 95 per

cent confidence intervals) of close to unity, particularly over longer time horizons.

Recent work by the Department (Department of Finance, 2019c) also highlighted a number of issues in

relation to the aggregate tax to output elasticity and principally the increasing prominence of CT receipts

in complicating standard measures. This work noted that taxes excluding CT appear to be more closely

aligned with movements in nominal GNI*, perhaps reflective of the volatility in both GDP and CT receipts

over the past decade.

Figure 23: aggregate tax to output elasticities, 1970-2018


0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

19

70

-85

19

70

-90

19

70

-95

19

70

-00

19

70

-05

19

70

-10

19

70

-15

19

70

-18

Tax to GNI*

Tax to GDP

sample period


Figure 24: tax to GNI* elasticities with a 95 per cent confidence interval, 1970-2018


In terms of tax forecasting methodology, the Department’s approach builds the forecast for total tax

revenues from the bottom upwards on a tax-by-tax basis. These elasticities nevertheless provide a

useful cross-check as the overall tax revenue number should be close to the projected growth rate in

nominal output, save in the cases of significant policy changes, one-off factors, or periods of unusual

growth.

5.4 Elasticities for the Main Tax Heads

Under the JRP, the Department has reviewed elasticities for different tax heads, specifically in terms of

estimation and interpretation. Some of these are detailed below in box 4, followed by the main findings

to date for income tax, USC, VAT and CT.

1.00

1.05

1.10

1.15

1.20

1.25

1970-85 1970-90 1970-95 1970-00 1970-05 1970-10 1970-15 1970-18

Sample period

Mid low high


Box 4: research on tax revenue elasticities The Department has published several related pieces of work on elasticities under the JRP. These include papers on tax

elasticities particularly in relation to income tax, USC and VAT, while a further paper on CT is ongoing. A revenue elasticity

estimates the automatic growth potential of a tax. It represents a useful baseline to judge the impact of discretionary tax

policy against, as the elasticity estimates how revenues respond to tax base growth under a ‘no policy change’ scenario,

hence the term automatic growth.

There are two main methods of estimation: a micro-data based analytical method and a macro-data based time-series

approach. The analytical approach, considered under the JRP work, involves defining how the design parameters of a tax

lead to revenues and linking this to the underlying tax base distribution. In this way, the determinants of the elasticity can be

easily understood and changes in these factors can be linked to changes in the elasticity over time.

Under the analytical approach, the revenue elasticity can be expressed as the ratio of the marginal tax rate to the average

tax rate. As such, it has a further interpretative function which goes beyond the tax forecasting context: it equates to a

measure of tax progressivity. Whenever the marginal tax rate exceeds the average tax rate, the average tax rate will increase

as the tax base increases. This is the most commonly used definition of progressivity in tax policy.

The trade-off between revenue volatility and progressivity can thus be summarised in a single number, the revenue elasticity.

For example, the income tax elasticity (across both PAYE and non-PAYE) is estimated to be 2.0, but the USC elasticity

(across both PAYE and non-PAYE) is estimated to be 1.2. Income tax is therefore more progressive than USC, but it is also

more sensitive to the economic cycle (in an upturn it increases by more than USC but, in a downturn, it falls by more). The

analytically constructed elasticities mean that design features of both taxes can be shown to play a role in this difference:

tax credits, which occur in income tax but not USC, contribute to progressivity by reducing average tax rates at the lower

end of the income distribution while also contributing to revenue volatility by increasing marginal tax rates at the income

point at which they are exhausted.

Knowing that a revenue elasticity can be expressed as the ratio of the marginal tax rate to the average tax rate is also helpful

for judging the revenue growth potential of any new tax or an existing tax for which a revenue elasticity has not been

empirically estimated. If the marginal tax rate and the average tax rate are the same, for example, then the revenue elasticity

will always be one.

Revenue elasticities are also useful to understand spill-overs between taxes, as the tax base can be expressed with respect

to secondary influences, which can include other tax policies. For example, the VAT revenue elasticity for the household

sector declined between the 1990s and the 2000s. One explanation for this is the introduction of a tax credit-based income

tax system in 2001 which increased the progressivity of the income tax system, but appeared to have a knock-on effect on

VAT elasticities (on the margin, a progressive income tax system reduces the gross income available for expenditure,

particularly for higher income households).

In summary, employing an analytical approach to revenue elasticities can result in greater clarity on the determinants of

revenue responsiveness, as well as allowing for a richer interpretation of the relationship between tax revenues and the tax

base.

5.4.1 Income Tax

An extensive study of income tax elasticities was conducted and published in 2017 (Acheson, et al

(2017)). The paper estimated elasticities over the period 2003 to 2013, using Revenue’s micro data on

the distributions of taxable income. There were a number of steps taken to estimate a range of income

tax elasticities both accounting for and not accounting for changes in income growth and the effects of

tax deductions. The paper highlighted the importance of thresholds and credits in effecting tax elasticity

estimates. The main results pointed to a highly progressive income tax system given that the overall

income tax elasticity was estimated at 2.0.

A range of USC revenue elasticities were also estimated. This pointed to an overall elasticity of 1.2,

much lower than the income tax elasticity. One interpretation from these results is that USC is more


stable but less progressive than income tax.35 A further interesting finding was that the USC estimates

for PAYE and non-PAYE earners were identical, unlike that for income tax. This reflects the fact that

the USC treats taxpayers more similarly than income tax (under income tax, an extra PAYE credit

explains the difference between the PAYE and non-PAYE elasticities). In recent budgets many of the

findings from this paper have been incorporated in the forecasting process, with the income tax and

USC elasticity estimates updated as summarised in table 10.

Table 10: income tax elasticities, coefficients in successive budgets

IT PAYE Wage

IT PAYE Employm

ent

IT Schedule D Wage

IT Schedule

D Employm

ent

USC PAYE Wage

USC PAYE

Employment

USC Schedule

D Wage

USC Schedule

D Employm

ent

Budget 2015

2.15 0.90 1.67 0.90 2.15 0.90 1.67 0.90

Budget 2016

2.15 0.90 1.67 0.90 2.15 0.90 1.67 0.90

Budget 2017

2.15 0.90 1.67 0.90 2.15 0.90 1.67 0.90

Budget 2018

2.10 n/a 1.40 n/a 1.20 n/a 1.20 n/a

Source: JRP research work.

5.4.2 VAT

In 2018, a comprehensive study on VAT revenue elasticities was published under the JRP (Acheson et

al (2018)). This work was motivated by the large weighting of VAT in tax revenue and the need to better

understand the relationship between VAT revenues and underlying activity as captured by the elasticity.

The approach in the paper modelled VAT revenues from the household sector as a function of the tax

system, incomes, savings and expenditures using five waves of the Household Budget Survey (from

1994/95 to 2015/16). Overall, two aggregate VAT elasticities were derived based on both income and

expenditure growth. The responsiveness of VAT to gross income is helpful for thinking about the impact

of income growth on VAT and income tax revenues combined, whereas the expenditure based measure

is more useful for VAT forecasting purposes.

Multiple factors impact on the VAT elasticity, including changes in earnings, savings, expenditure

patterns and both VAT and income tax policies. Intuitively, the VAT elasticity increases if marginal

income tax rates decline, as households have more to spend on the margin (holding

savings/expenditure pattern behaviour constant). The elasticity falls as savings rise, as households

spend less on the margin.

The work provided rich insights into the VAT elasticity, particularly the fact that the elasticity was likely

to vary over time, unlike income tax and USC counterparts. The spillovers from the income tax system

to VAT and the importance of changing saving patterns were identified as important explanations for

the varying elasticity over time (more so than changing expenditure patterns). The results from this work

suggested a lower elasticity of 0.7 (relative to the Department’s value of one), however this was not

35 USC was still progressive as the elasticity exceeded unity.


deemed suitable for the Department’s current forecasting approach as it was based solely on household

final expenditure (whereas the public sector also pays VAT and not all business will pass VAT on to the

final consumer). Overall, this pointed to the need for further work on the elasticity before any firm

recommendations were made.

5.4.3 Corporation Tax

The Department is engaged in ongoing research examining CT elasticities under the JRP. This research

aims to broaden the existing suite of micro-founded revenue elasticity papers produced by the

Department. Previous research in this area has been macro-founded and derived dynamic CT

buoyancy estimates using OECD panel data controlling for both discretionary policy changes and

business cycle fluctuations. The current project uses data on profit distributions to estimate elasticities.

Currently, the CT revenue elasticity is assumed to be one for forecasting purposes. Given that a revenue

elasticity can be expressed as the proportional change in revenue divided by the proportional change

in the tax base, the currently used value for the elasticity implies that the marginal tax rate equals the

average tax rate for CT. However, there are many reasons why the average tax rate would be lower

than the marginal tax rate for CT taxpayers (as in other tax heads) – for example the use of tax credits

such as the foreign tax credit or the R&D tax credit which will lower the net tax liability. Similarly, reliefs

such as double taxation relief would have the same effect. On the basis of this logic, the Group

considered that it is advisable to revise the CT revenue elasticity upward. Current research under the

JRP aims to provide a more definitive value for the elasticity making use of tax returns data. The Group

recommends waiting until the project results are available and tested before formally changing the

elasticity, but agreed that it is reasonable to assume an upward revision in the near future.

5.5 Lessons from Quarterly Data

An alternative to the analytical method above, centres on the analysis contained in the Department’s

annual taxation reports (Department of Finance, 2019a) and specifically co-movements between the

main tax heads and underlying macroeconomic series. In the figures below, taxes on a quarterly basis

(annualised) are plotted relative to a number of key macroeconomic variables over an extended sample

(2001 to 2019). This alternative approach is informative for a range of reasons, both in terms of

identifying tax trends, movements in effective rates and also in inputting into annual budget forecasts.

In figure 25, income tax receipts are plotted against compensation of employees. The figure highlights

the relationship between the two series. The ‘step change’ in the graph coincides with the crisis and

consolidation years, when taxes rose disproportionately in relation to wages as a result of policy-

induced changes. The strong recovery in the labour market since 2012 has had a favourable impact on

the income tax yield, with employment close to a fifth higher than the low point during the crisis. More

recently, wage inflation has also contributed to income taxes with the cumulative increase in income

taxes closely aligned with compensation of employees.


Figure 25: income taxes and employee compensation, annualised data in logs


In figure 26, CT receipts are plotted, again on an annualised basis, relative to a profitability series, as

captured here by net operating surplus. The upward trajectory in CT has been well documented in

recent years with economy-wide profitability measures also rising.

Figure 26: corporation taxes and net operating surplus, annualised data in logs


In figure 27, VAT receipts are plotted against nominal personal consumption expenditure. Two distinct

patterns are evident. In the lead up to the financial crisis (red-dashed line), VAT receipts increased

rapidly partly in response to VAT received from new housing. If looked at in terms of level (rather than

logs), VAT receipts as a share of personal consumption peaked at 16 per cent during this period. The

second-post crisis era (blue dashed line) shows spending that is less VAT rich, with the share falling

y = 1.2106x - 3.9861R² = 0.7374

8.8

9.0

9.2

9.4

9.6

9.8

10.0

10.6 10.7 10.8 10.9 11.0 11.1 11.2 11.3 11.4 11.5

inco

me

tax

compensation of employees

y = 0.7626x + 0.0652R² = 0.6176

8.0

8.2

8.4

8.6

8.8

9.0

9.2

9.4

10.6 10.8 11.0 11.2 11.4 11.6 11.8

corp

ora

tio

n t

ax

net operating surplus


towards 14 per cent, reflecting weaker non-discretionary spending (which typically faces the standard

rate of VAT), an increase in the level of VAT-exempt spending, and more subdued construction levels.

Figure 27: VAT and nominal consumption, annualised data in logs


In figure 28, excise receipts are plotted against goods related consumption spending (in real terms).

The relationship between the series has been affected in recent years by the introduction of ‘plain

packaging’ for tobacco related products. The latter resulted in a front loading of excise payments in

2017 and a consequent decline in 2018.

Figure 28: excises and consumption of goods, annualised data in logs


The quarterly analysis outlined above provides an additional information base to the Department when

compiling annual tax forecasts. This analysis can form an important part of the judgement variable in

the Department’s standard forecasting equation, as set out in Chapter 3.

y = 0.9058x - 0.8925R² = 0.7364

8.9

9.0

9.1

9.2

9.3

9.4

9.5

9.6

9.7

10.8 10.9 11.0 11.1 11.2 11.3 11.4 11.5 11.6

VA

T

nominal consumption

y = 0.9852x - 1.7349R² = 0.7056

8.3

8.3

8.4

8.4

8.5

8.5

8.6

8.6

8.7

8.7

8.8

10.2 10.3 10.3 10.4 10.4 10.5 10.5 10.6 10.6

exci

se d

uti

es

consumption of goods


Chapter 6 Forecasting Recommendations

6.1 Introduction

This Chapter summarises the main deliberations of the TFMRG in relation to the four largest tax heads.

The main goal was to propose improvements to the forecasting methodologies where appropriate. In

some cases, there appears to be a need to change existing approaches, however this was not always

clear-cut particularly where a balance needs to be struck between complexity and parsimony.

6.2 Income tax and USC

Income tax forecasts were found to be reasonably accurate and amongst the best performing tax heads.

The forecasting approach within the Department is disaggregated, particularly in comparison to the

other large tax heads. Aside from the disaggregated approach, more aggregative data, such as the

relationship between income tax receipts and economy-wide compensation figures (depicted in Chapter

5) helps in terms of parsimony and are a useful cross-check of the income tax forecasts.

The Group welcomed the research undertaken and published in relation to income tax (including USC)

elasticities (Acheson et al, 2017). This work has facilitated forecasts and the new elasticities were

deemed to better capture compositional changes within the labour market given the use of distributional

income data. This work also negated the need for a separate employment elasticity. The Group noted

that the annual estimates of the elasticities were relatively stable, despite covering a period of labour

market expansion and contraction over 2003-2013.

While not a major topic of discussion in the Group, the quality and reliability of data in relation to the

labour market are also high. These data are less likely to be revised and are deemed to capture

underlying dynamics well, thereby, facilitating income tax forecasts. The Group also saw merit in

progressing the work undertaken in relation to the elasticity of taxable income (ETI). Further work in this

area could explore the potential to incorporate behavioural responses to tax policy changes, alongside

the standard static tax forecasts.

Overall, the Group welcomed the new research on income tax including USC elasticities and the

continued use of these elasticities going forward.

6.3 VAT

The Group noted the consistently strong performance of VAT forecasts over the past decade. Most of

the discussions focused on the macroeconomic driver (personal consumption), the composition of VAT

revenue, the role of online purchases and the housing market. The main concerns in relation to the

macroeconomic driver centred on its coverage as it includes food, rents and many items that are zero-

rated or exempt from VAT. Notwithstanding this, however, the Group underlined that the existing

macroeconomic driver was performing well and should be maintained. The Group also discussed the


potential for spillovers in income tax policy, and changes in savings patterns and consumption

preferences to affect the VAT elasticity over time.

In the 2008 report, the role of the housing market on VAT forecasts featured heavily. Following this, the

Department’s VAT forecasting methodology was modified to include a proxy for new housing market

activity and related VAT. However, this approach was discontinued after Budget 2011 following the

marked downturn in the sector.36

Despite the strong VAT forecasting performance, the Group was of the opinion that the main VAT

forecast should be supplemented to include a housing market element given recent growth in the sector

and prospects going forward. One proposal (detailed in Annex C) involved splitting VAT into housing

and non-housing components (similar to the approach undertaken for the 2008 report). The former was

compiled based on data available from the Property Price Register (which is based on Revenue stamp

duty tax returns). This sub-component in turn is driven by forecasts for the housing market as proxied

by budget day projections for investment in dwellings. This dis-aggregated approach was tested from

Budget 2011 to Budget 2018 and yielded some improvements in terms of forecast accuracy. That said,

given the relatively small weight of housing related transactions, the impacts were small, albeit more

sizable in recent years as the housing sector has grown. For these reasons, the Group recommends

that future VAT forecasts be supplemented with a housing component.

6.4 Corporation Tax

Discussions on CT focused on the sizable forecasting errors, particularly in recent years. The pattern

of over-performance was discussed and whether this was indicative of either prudent bias in the forecast

or more structural factors. There was a strong consensus that CT forecasts needed to be improved

while a broad recognition that this was extremely challenging for well documented reasons. Notably,

the concentrated and lumpy nature of receipts and the difficulty in predicting corporate profits. Aside

from these factors, the Group discussed the role of allowances, credits, BEPS, losses and their potential

impact on forecasts.

The Group also recognised that CT remains a difficult tax to forecast internationally. Work by the Office

for Budget Responsibility in the UK (OBR, 2017) highlighted particular issues with CT with nearly two-

thirds of the gap between tax forecasts and outturns in 2015/16 accounted for by this tax head

(specifically, onshore related receipts including accounting treatments). The OBR also noted issues

around company specific factors that influences yields and a tendency to under predict outturns. Other

work more garnered to small open economies, such as research for Sweden (Shahnazarian and

Solberger, 2017) also noted high corporate tax forecast errors.

The Group discussed ongoing Department work in relation to CT elasticities (Chapter 5). An overarching

aim of this research project is to derive more data-driven estimates of the elasticity, which exploit

36 For example, data on housing completions (as proxied by ESB connections) show a decline from a peak 93,419 units in 2006 to a low of 8,301 units in 2013.


information from the distribution of taxable income, and to see how it performs against the currently

used elasticity of one.

In the meantime, the Group carried out a series of tests for CT using higher retrospective elasticities. A

summary of these results is shown in Annex C. Looking at the past five years, there appears to be merit

in moving to a higher elasticity, in the region of 1.2 to 1.3, although a cautious and prudent approach is

warranted given the uncertainty in forecasting CT. Ultimately, the elasticity will be informed by ongoing

JRP work and more aggregative analysis on long-run trends between CT and profitability.

The Group also noted recent work published in the area of CT modelling by the Department

(McGuinness and Smyth, op.cit,). This paper highlighted a number of challenges in modelling CT in

part due to the concentrated nature of receipts in Ireland. In relation to forecasting however, the authors

highlighted that in-year estimates for CT tended to outperform a range of econometric models. This re-

affirms the importance of in-house knowledge, judgement and expertise within the Department and

Revenue in relation to CT. The research also pointed to benefits arising from the use of error correction

and vector error correction models as checks on year-ahead forecasts.

While the Group also explored alternative macroeconomic drivers (including net operating surplus),

there was a strong view that CT remained a particularly difficult tax to forecast with much of the errors

linked to the underlying macro driver. In light of the recent pattern of forecast errors, the Group

recommended the use of a higher elasticity and, in the absence of a notable better-performing macro,

the continued use of gross operating surplus.

6.5 Excise Duties

The mixed performance of excises and the heterogeneous nature of this tax was noted. The Group felt

that a more disaggregated forecast is warranted focusing on the main sub-components – alcohol, fuels

and tobacco. These items are also highly susceptible to policy changes and behavioural effects. Going

forward it is recommended that the current approach within the Department is supplemented with a

more disaggregated bottom-up approach building on Revenue’ s estimates from detailed excise data.

6.6 Other Taxes

Forecast errors are typically larger for the smaller tax heads, specifically capital taxes. The difficulty in

forecasting these taxes was noted to be principally due to the lack of appropriate macroeconomic

drivers given their transactions based nature. At the same time, forecasts are also heavily affected by

a relatively small number of transactions, many of which arise at end-year. These taxes are also

influenced by equity and asset market developments, which are inherently unpredictable. Finally, given

the impact of Brexit on trading arrangements, the Group advised on the need to closely monitor customs

duties and consider developing new approaches in the near-term. Also, in relation to Brexit the Group

noted the links between the accuracy of tax forecasts and economic turning points, suggesting the need

for an even greater degree of caution in tax forecasting in the near-term.


6.7 Other Recommendations

The long-run tax to output elasticity should be close to unity. While the overall elasticity of close to one

still holds on the basis of both nominal GDP and GNI*, there has been a fall in the elasticity over time.

While tax forecasts are made on a bottom-up basis, it is recommended that a more formalised

aggregative tax forecast be prepared in conjunction with the disaggregated approach. Any significant

divergences between the two methods should be explained and documented.

Given the importance of general government metrics and the extent to which international agencies

forecast on this basis, the Group advised that the Department continue to develop its general

government tax forecasting capability. It is hoped that this could be a stand-alone exercise although

clearly linked to the exchequer based tax forecasts. The Group also felt that this should be easier to

facilitate given much richer data sources and modelling capabilities than were the case at the time of

the last Report.

6.8 Summary of Recommendations

Income Tax:

The Group endorsed the new income tax and USC elasticities. The Group also advised

that the elasticities be reviewed and re-estimated on a more periodic basis, to reflect the

latest labour market data.

VAT:

The Group recommends that existing forecasts for VAT be supplemented with a housing

specific component drawing on expected trends in the housing market.

Corporation Tax

Consideration should be given to increasing the elasticity used in the corporation tax

forecasts from its current value of one. In time, the elasticity should be informed by the

ongoing JRP research work in this area.

For in-year estimates, the ongoing and detailed exchange of information between

Revenue and the Department of Finance remains an integral part of the forecasting

process. For year-ahead estimates, future forecasts should be informed by newly

developed error correction models within the Department.

Excise Duties

The Group recommends moving to a new more disaggregated, bottom-up approach for

forecasting excise duties with a focus on the main sub-components - alcohol, fuels and

tobacco.


Other Taxes

Given the potential changes to the future trading relationship between the United Kingdom

and the European Union, the Group recommended placing a greater focus on the

forecasting of customs duties.

Given the uncertainties surrounding the impact of Brexit on the Irish economy and the

strong links between economic turning points and the accuracy of tax forecasts, the Group

suggested the need for caution in the near-term.

Other Recommendations

The Group recommends that a formalised aggregative (top-down) tax forecast be

prepared in conjunction with the disaggregated approach. Any significant divergences

between the two forecasts should be explained and documented.

Given the importance of general government aggregates, the Department should continue

to develop its general government forecasts.


Chapter 7 Conclusion

This is the third methodological review of the Department of Finance’s approach to tax forecasting with

a focus on tax developments over the past decade. Looking at recent literature in relation to tax

modelling both within Ireland and abroad, as well as approaches at institutions such as the Central

Bank of Ireland and the European Commission, the Group was of the view that the Department’s

approach was in line with and similar to international practice.

Over the past decade (the period since the last Report), the accuracy of forecasts was heavily skewed

by the scale of the economic and financial crisis that ensued from 2008. Excluding this period, the

Department’s tax forecasts performed reasonably well, although there has been a tendency for

forecasts to err on the low side.

Income taxes and VAT forecasts have performed well over the past decade with no major changes

advised. That said, in relation to income tax (including USC), recent research in relation to elasticities

has facilitated a greater understanding of tax developments and has assisted forecasts. VAT forecasts

should, however, be supplemented with a more disaggregated approach that explicitly recognises

housing market developments.

More recent years have been marked by large forecasting errors in relation to CT. Going forward, the

Group recommends that a higher elasticity be used for CT. For excises, there appears to be strong

merit in the Department adopting more of a disaggregated approach in generating an overall forecast.

Finally, in light of the increasing prominence of general government aggregates, the Group

recommends that the Department continues to develop more stand-alone general government tax

forecasts. These would provide a useful cross-check of the budget day exchequer based tax forecasts.


Annex A: Group Membership and Meetings

The Group was comprised of officials from the Department of Finance and external bodies - the Central

Bank of Ireland, the Revenue Commissioners and the European Commission.

The Group consisted of: Matt McGann (Chair), David Hughes, Gerard McGuinness, Eimear Nolan, Leo

Redmond and Diarmaid Smyth (all Department of Finance); Rónán Hickey and Linda Kane (Central

Bank of Ireland); Keith Walsh, Fionnuala Ryan and Jean Acheson (Revenue Commissioners); Simona

Pojar and Allen Monks (European Commission).37

The Department hosted seven meetings between April and November, with meetings focusing on

specific tax heads, with the structure as follows:

Meeting 1 (26 April): introduction, terms of reference, tax forecasting performance.

Meeting 2 (24 May): approaches to forecasting in the European Commission, the Central Bank

of Ireland and the Revenue Commissioners. VAT – forecasting approach and performance.

Meeting 3 (11 June): VAT – elasticities and recommendations. Corporation tax - forecasting

approach and performance.

Meeting 4 (21 June): Corporation Tax – elasticities, micro data and recommendations. Income

Tax – forecasting approach and performance.

Meeting 5 (05 July): Income Tax – elasticities and recommendations. Excise Tax – forecasting

approach, performance and recommendations.

Meeting 6 (25 September): Capital taxes and Pay Related Social Insurance – forecasting

approach, performance and recommendations.

Meeting 7 (19 November): Drafting of report, main findings, recommendations and sign-off for

publication.

37 The group would also like to acknowledge the contribution of Thomas Conefrey (Central Bank of Ireland), Philip O’Rourke and Donnchadh O’Donovan (Revenue Commissioners).


Annex B: Forecast Summary Decomposition

The Table below summarises the decomposition exercise from Chapter 4. Overall, forecast errors were

noted and then adjusted for the correct starting point, the removal of any judgements/adjustments and

the most recent macroeconomic outturn. This was done on sequential basis. Taking the case of

judgement as an example, if the removal of judgement from the equation led to a larger error, we can

assert that judgement improved the forecast (and vice-versa) – thereby “y” for yes is inserted in the

Table. Similarly, for the starting point, if the use of the correct starting point (the outturn) resulted in a

lower error, than we insert “y” to note that it improved the forecast. The final column reports the

percentage of times that either the correct starting point, the most recent macroeconomic driver and the

use of judgement improved the forecast.

Table B1: forecast decomposition summary

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 %

Did the starting point improve forecasts? Yes (y) or No (n)

Income tax n y y n y n y y n n y 55

VAT y y n y y y n y y n n 64

Corporation tax y n y y n y n y y n y 64

Excise n y y n y n y n n y n 45

Did the macro driver outturn improve forecasts? Yes (y) or No (n)

Income tax y y n n n y n n y n n 36

VAT y y n y y y y n n y y 73

Corporation tax y y y n n n y y n y y 64

Excise y y n n y n y y y y n 64

Did judgement improve forecasts? Yes (y) or No (n)

Income tax n y y y n y y y y y y 82

VAT y y n y n n y y n n y 55

Corporation tax y y n y n y y n y n y 64

Excise y y y y y y n n n y y 73



Annex C: Alternative Forecasting Approaches

In the approaches documented below, budget day year-ahead projections were adjusted to reflect

alternative approaches involving different macroeconomic drivers or elasticities. In order to try and

maintain comparability with budget forecasts, changes were made on a sequential basis with all other

inputs left unchanged looking back at recent budgets.

VAT Forecasts

To investigate the role of housing in shaping VAT forecasts, the existing methodology was

supplemented with a housing, non-housing split. For housing, the Property Price Register details VAT

receipts on new housing transactions. This is available from 2010. These receipts on a year-ahead

basis are then driven by Budget day forecasts for activity in the housing market as proxied by the growth

rate and deflator for investment in new dwellings. The non-housing related element is driven by the

existing macroeconomic driver (consumer spending). The results are reported in the table C1 below.38

In 3 of the 5 years, the forecasts with housing were closer to the outturns although improvements were

relatively minor.

Table C1: retrospective VAT forecasts, year-ahead basis

€ billions 2014 2015 2016 2017 2018

Outturn 11.15 11.94 12.42 13.30 14.23

Budget forecast 10.74 11.77 12.86 13.37 14.09

Error 0.41 0.17 -0.44 -0.07 0.14

Forecast with housing included 10.75 11.79 12.89 13.41 14.15

Error 0.40 0.15 -0.47 -0.11 0.08

Improvement (Yes (y)/No (n)) y y n n y

Note: numbers may not sum due to rounding.


38 The exercise was tested back to Budget 2011. The mean absolute error for the VAT forecast adjusted for housing was marginally lower than for the unadjusted version.


CT Forecasts

For CT, a number of alternative approaches were investigated, a subset of which are shown below. The

first table (C2) show forecasts using an alternative higher elasticity with gross operating surplus.39 This

led to an improvement in forecast accuracy although improvements at times were marginal. The second

table (C3), looks at CT forecasts based on outturns for gross and net operating surplus, keeping the

elasticity at unity. While forecast errors were smaller relative to budget day projections, errors remain

large. This re-affirms the difficulties with the macroeconomic driver. While CT remains hard to predict,

there appears to be merit in moving away from an elasticity of unity towards a higher number in future

budgets.

Table C2: retrospective CT forecasts – alternative elasticity estimates, year-ahead basis

€ billions 2014 2015 2016 2017 2018

Outturn 4.61 6.87 7.35 8.20 10.39

Budget forecast 4.38 4.58 6.61 7.72 8.50

Error 0.23 2.30 0.74 0.48 1.88

Forecast with elasticity of 1.3 4.41 4.64 6.73 7.81 8.59

Error 0.21 2.23 0.62 0.39 1.79

Improvement (y/n) y y y y y

Note: numbers may not sum due to rounding. Source: Department of Finance.

Table C3: retrospective CT forecasts – alternative macroeconomic drivers, year-ahead basis

€ billions 2014 2015 2016 2017 2018

Outturn 4.61 6.87 7.35 8.20 10.39

Forecasts with outturns for gross and net operating surplus (elasticity of 1.0)

GOS outturn 4.77 7.00 6.29 8.24 9.09

Error -0.15 -0.12 1.06 -0.04 1.30

NOS outturn 4.83 5.98 6.13 7.84 8.90

Error -0.21 0.89 1.23 0.36 1.48

Note: numbers may not sum due to rounding. Source: Department of Finance.

39 The elasticity of 1.3 is based on long-run movements between the tax head and macro driver. However, the

latest available information for CT (the 2017 CT returns) indicates an average tax rate, in the aggregate, of 10 per cent. Given the revenue elasticity is the ratio of the marginal tax rate to the average tax rate, this implies an aggregate elasticity of 1.25 (or 1.3 after rounding). Aggregate data available at: https://revenue.ie/en/corporate/information-about-revenue/statistics/income-distributions/ct-calculation.aspx

https://revenue.ie/en/corporate/information-about-revenue/statistics/income-distributions/ct-calculation.aspx


References

Acheson, J, Deli, Y, Lambert, D, and Morgenroth, E, (2017): Income Tax Revenue Elasticities in Ireland:

An Analytical Approach, ESRI Working Paper, No. 59, The Economic and Social Research Institute

(ESRI), Dublin.

Acheson, J, Deli, Y, Morgenroth, E, Lambert, D, and Murphy, M, (2018): VAT revenue elasticities: An

analytical approach, ESRI Working Paper, No. 596, The Economic and Social Research Institute

(ESRI), Dublin.

Addison-Smyth, D and McQuinn, K. (2010), Quantifying revenue windfalls from the Irish housing

market, Economic and Social Review, Vol 41. No. 2, Summer 2010.

Addison-Smyth, D and McQuinn, K. (2016). Assessing the sustainable nature of housing-related

taxation receipts: the case of Ireland, Journal of European Real Estate Research, Vol 9. No. 2, 2010,

pp. 193-214.

Amaglobeli, D, Crispolti, V, Dabla-Norris, E, Karnane, P., and Misch, F. (2018): Tax Policy Measures in

Advanced and Emerging Economies: A Novel Database, IMF Working Paper, April, 2018.

Australian Treasury (2012): Review of Treasury Macroeconomic and Revenue Forecasting, December

2012.

Bank of England (2015): Evaluating Forecast Performance, Independent Evaluation Office, November.

Casey, E and Smyth, D. (2016): Revisions to Macroeconomic Data: Ireland and the OECD, The

Economic and Social Review, Vol 47, No 1, Spring.

Conroy, N. (2019), Estimating Ireland’s Tax Elasticities: A Policy-Adjusted Approach, Irish Fiscal

Advisory Council, Working Paper, No. 10, June 2019.

Department of Finance (2008): Report of the Tax Forecasting Methodology Review Group, February.

Department of Finance (2018): GDP and Modified GNI – Explanatory Note, May.

Department of Finance (2018b): The Elasticity of Taxable Income. December.

Department of Finance (2019a): Annual Taxation Report, March.

Department of Finance (2019b): Stability Programme Update, April.

https://www.esri.ie/system/files?file=media/file-uploads/2017-03/RS59.pdf

https://www.esri.ie/system/files?file=media/file-uploads/2017-03/RS59.pdf

https://www.esri.ie/publications/vat-revenue-elasticities-an-analytical-approach

https://www.esri.ie/publications/vat-revenue-elasticities-an-analytical-approach

https://www.imf.org/en/Publications/WP/Issues/2018/05/11/Tax-Policy-Measures-in-Advanced-and-Emerging-Economies-A-Novel-Database-45870

https://www.imf.org/en/Publications/WP/Issues/2018/05/11/Tax-Policy-Measures-in-Advanced-and-Emerging-Economies-A-Novel-Database-45870

https://treasury.gov.au/sites/default/files/2019-03/forecasting-review.pdf

https://www.bankofengland.co.uk/-/media/boe/files/independent-evaluation-office/2015/evaluating-forecast-performance-november-2015.pdf?la=en&hash=C13B3C96125391E0387B89D8DB40AE222F684EF6

https://www.esr.ie/article/view/505

https://www.fiscalcouncil.ie/wp-content/uploads/2019/06/Estimating-Irelands-Tax-Elasticities-Niall-Conroy-Irish-Fiscal-Advisory-Council-Working-Paper.pdf

http://s3.amazonaws.com/zanran_storage/www.finance.gov.ie/ContentPages/8987749.pdf

https://assets.gov.ie/7746/30220ad9d54748a2b155a9e0a82b73b1.pdf

https://www.gov.ie/en/publication/b44035-elasticity-of-taxable-income-december-2018/

https://assets.gov.ie/7290/750c0d5c58d840c5a811bbb557684966.pdf


Department of Finance (2019c): Addressing Fiscal Vulnerabilities, Budget 2020, October.

European Commission (2013): Discretionary tax measures: pattern and impact on tax elasticities,

European Economy, Economic Papers 499, May.

Hannon, A., Leahy, E. and O’Sullivan, R. (2016): An Analysis of Tax Forecasting Errors in Ireland, The

Economic and Social Review, Vol 47, No 3, Autumn.

Keene, M and Thomson, P., (2007) An Analysis of Tax Revenue Forecast Errors, New Zealand

Treasury, Working Paper 07/02, March.

McGuinness, G and Smyth, D. (2019): Modelling Recent Developments in Corporation Tax, Department

of Finance, Working Paper, November.

Office for Budget Responsibility (2018): Forecast Evaluation Report, December.

Parliamentary Budget Office (2019): An introduction to tax revenue buoyancy, PBO Publication 15 of

2019, Revenue Analysis Series.

Shahnazarian. H., and Solberger, M., (2017): Forecasting and analysing corporate tax revenues in Sweden using Bayesian VAR models, Finnish Economic Papers, Volume 28, Number 1, Fall 2017.

http://budget.gov.ie/Budgets/2020/2020.aspx

https://ec.europa.eu/economy_finance/publications/economic_paper/2013/pdf/ecp499_en.pdf

https://www.esr.ie/article/view/609

https://treasury.govt.nz/sites/default/files/2018-10/twp07-02-pt1.pdf



https://obr.uk/docs/dlm_uploads/Forecast-Evaluation-Report-2018_Web_Accessible.pdf

https://data.oireachtas.ie/ie/oireachtas/parliamentaryBudgetOffice/2019/2019-03-25_an-introduction-to-tax-revenue-buoyancy_en.pdf

https://www.taloustieteellinenyhdistys.fi/wp-content/uploads/2017/10/FEP_1_17_4_Spanberg.pdf

https://www.taloustieteellinenyhdistys.fi/wp-content/uploads/2017/10/FEP_1_17_4_Spanberg.pdf


Tithe an Rialtas. Sráid Mhuirfean Uacht, Baile Átha Cliath 2, D02 R583, Éire Government Buildings, Upper Merrion Street, Dublin 2, D02 R583, Ieland T:+353 1 676 7571 @IRLDeptFinance www.gov.ie/finance

Date post:	10-Feb-2022
Category:	Documents
Upload:	others
View:	15 times
Download:	0 times