Central Bank of Swaziland (CBS) Research Paper Commissioned by the Common Market for Eastern
and Southern Africa (COMESA).
Effects of Fiscal Policy on the Conduct and Transmission of Monetary Policy in Swaziland.
Policy Research and Macroeconomic Analysis Division of the Central Bank of Swaziland Policy Research and Statistics Department1
September 2015.
Abstract
The estimated VARs Johansen Cointegration approach are found to be stable and the Wald tests rejects all short run impacts of the fiscal balance on inflation and discount rate differential between Swaziland and South Africa. The fiscal balance has a long run significant impact on the inflation rate where a rise in the fiscal balance by a percentage point leads to a fall in inflation in the long run by 0.13 percent. The Fiscal Balance improvement leading to a fall in inflation widens the interest rate differential between South Africa and Swaziland lowering domestic interest rates in the process significantly in the long run. Fiscal shocks emanate from external factors with South Africa inflation having strong causality effects on the fiscal balance in the short run with a chi-sq. of 16.37 and a low p-value of 0.0003 in the Wald short run causality test. The fiscal balance is not financed through money creation in the short run where money creation has a chi sq. of 3.20 and p value of 0.20 as shown in the Wald tests but fiscal shocks would still lead to persistently high inflation and interest rates only to stabilise in the tenth year. The financial crisis and introduction of value added tax are found, tough insignificant, to reduce and increase inflation in the short run by 0.19 and 0.36 percent respectively and have no long run impact. The variance decomposition of inflation is mainly driven by the discount rate and fiscal balance in the long run.
This Research Paper should not be reported as representing the views of the CBS. The views expressed in this Research Paper are those of the author(s) and do not necessarily represent those of the CBS or CBS policy.
1 Prepared by: Simiso F. Mkhonta. Authorised for distribution by:
1
Contents Page
1. Introduction……………………………………………………………………………………………………………..4(a) Inflation and the Fiscal Deficit in 1980-2000………………………………………………………4(b) Inflation and the Fiscal Deficit in 2000-2015………………………………………………………5
2. Review on Fiscal Performance………………………………………………………………………………….63. Review on Existing Legal and Institutional Developments Discouraging and Promoting Fiscal
Dominance and Effective Coordination of Monetary and Fiscal Policy……………………..84. Challenges of Existing Fiscal Policy……………………………………………………………………………95. Key Features of the Operational Framework for Fiscal Policy, Monetary Policy and
Interaction between Fiscal and Monetary Policy……………………………………………………..116. Literature Survey……………………………………………………………………………………………………16
6.1 Theoretical and Empirical Literature on the Different Channels through which Fiscal Policy can affect Monetary Policy………………………………………………………………………16
6.2 The Impact of Fiscal Policy on Monetary Policy………………………………………………….166.3 Fiscal Policy Impact on Monetary Policy under Fixed Exchange Rate………………….16
6.4 Economic Mechanical Operation of Fixed Exchange Rate Regime…………..247. Methodology and Data……………………………………………………………………………………………..268. Econometric Results………………………………………………………………………………………………….27
8.1 Units Root Tests………………………………………………………………………………………………….278.2 The Long Run Inflation Estimation Equation and Diagnostic Tests……………………….298.3 The short Run Inflation Estimation Equation and Diagnostic Tests………………………388.4 The Long Run Discount Rate Differential Equation Estimation and Diagnostic
Tests…………………………………………………………………………………………………………….408.5 The Short Run Equation on Discount Rate Differential……………………………………….428.6 The Short Run Value Added Tax and Financial Crisis on Inflation Estimation
Equation……………………………………………………………………………………………………..469. Summary and Policy Implications…………………………………………………………………………….48
References
Figures
1. The Timeline for Growth and Fiscal Performance……………………………………………………62. Government Salaries and Capital Expenditure and Revenues………………………………….73. AR Root Stability Tests Equation 8(i)………………………………………………………………………334. Excess Liquidity………………………………………………………………………………………………………34 5. Impulse Response Function Equation 8(i)……………………………………………………………….356. Crude Estimation of the Trend for Velocity of Money…………………………………………….377. AR Root Stability Test Discount Differential Equation……………………………………………..428. Impulse Response Function; Discount rate Differential Equation……………………………449. Value Added Tax and Inflation………………………………………………………………………………..48
Tables
2
1. ADF Statistics for Testing for Unit Root……………………………………………………………….28.
2. Lag length Criterion………………………………………………………………………………………………29
3. Johansen Unrestricted Cointegration Rank Test (Trace)…………………………………………30
4. Johansen Unrestricted Cointegration Rank Test (Maximum Eigenvalue)………………..31
5. Diagnostic Tests for Inflation Equation…………………………………………………………………..32
6. Wald Causality Test……………………………………………………………………………………………397. Diagnostic Test for Discount Rate Differential Equation……………………………………..418. Discount Rate Differential Equation Stability Test……………………………………………….429. Wald Causality Test Discount Rate Differential Equation…………………………………….4510. Diagnostic Results Value Added Tax and Financial Crisis Equation……………………….47
Appendix I Definitions and Sources of Variables……………………………………………………………..50
Appendix II Tables and Figures……………………………………………………………………………………....50
Table A111 Cointegrating Equation –Johansen Test; Longrun equation –for Inflation. VAR
Estimation and Diagnostic Tests…………………………………………………………………………………….50
Table A112 Cointegrating Results of Long Run Differential Equation …………………………….55
Table A113 Wald Short Run Causality Test for Discount Rate Differential …………………….57
Table A11 4 Block/Joint VAR Test for Causality…………………………………………………………….59
Table A115 VAR Heteroskedasticity Test Results …………………………………………………………60
Table A116 Short Run Inflation Equation (VECM)…………………………………………………………61
Table A117 Final VAR Stability Test for Inflation Equation……………………………………………63
Table A118 Deficit Testing for Short-run Effects …………………………………………………………66
Table A119 Exogeniety Tests ………………………………………………………………………………………68
References………………………………………………………………………………………………………………….73
1. INTRODUCTION
3
Swaziland has not experienced a deficit of greater than 10 percent, the highest being 9.5
percent experienced in fiscal year 2010/11 during the height of the financial crisis, since the
1980s. Fortunately the monetary authorities have not been apt to finance the deficit
through money creation. Swaziland has seen prudent monetary policy owing to the
membership of the country to the Common Monetary Area (CMA). Botswana, Lesotho and
Swaziland due to their close proximity and trade links with South Africa formed the Rand
Monetary Area (RMA) in 1974 together with South Africa and later Botswana opted out of
the monetary union in 1975. The membership to the CMA requires Swaziland to maintain
the pegged exchange rate with the Rand which would require Swaziland to exercise prudent
fiscal policy. The tendency of fiscal deficits to increase money supply and put pressure on
the exchange rate encourage the fiscal authorities to limit the deficits they run both in
magnitude and frequency. The Government of Swaziland though did not live up to
expectations when a deficit of 9.5 percent was run coupled with minimal reforms on the
expenditure side during the height of the crisis. The Government instilled the impression
that should an environment of low revenue persist then debt would rise to a level where
there is a credit crunch and default leading to a serious contamination of the economy to a
wider scale. The credit that was extended to Government in 2012 as a measure to
ameliorate the effects of the global financial crisis was quickly reversed in 6 months to
maintain an environment conducive for the existence of the CMA and financial stability but
persistence of low revenues would see government defaulting until there are serious
measures to curb expenditure in particular the huge wage bill.
(a) Inflation and the Fiscal Deficit in 1980-2000
Swaziland experienced a cyclone domoina and a severe drought in the 1980s resulting in
government running deficits which apparently were inflationary given that in the 1980 the
highest inflation figures of 20 percent were recorded though the deficit was not finance
through money creation. The high inflation locally could also be traced from inflation
development in Swaziland’s major trading partner South Africa. P. Burger and M. Marinkov
(2001) found that the periods of high inflation (1980-89 and 1990-2000) were periods
before the introduction of inflation targeting where inflation was implicitly targeted and
there was less success in containing inflation. Wolassa L. Kumo (2015) also concluded that
by adopting the inflation targeting monetary policy framework since 2000, South Africa
4
succeeded in achieving low and stable general price level after a pre-inflation targeting
regime period covering 1960Q1-1998Q4 where South Africa adopted various monetary
policy frameworks including exchange-rate targeting, discretionary monetary policy,
monetary-aggregate targeting and an eclectic approach resulting in high and more volatile
inflation which was imported into Swaziland. The high inflation rate in Swaziland in the
1980s were therefore not due to fiscal deficits but was mainly imported from South Africa
which was still finding its footing in monetary policy.
(b) Inflation and the Fiscal deficit 2000-2015
The inflation trajectory moderated post 2000 after South Africa implemented inflation
targeting in 1999, due to the great moderation. In the early 2000s the Government ran fiscal
deficits after the Dotcom Stock bubble of 2000 which resulted in suppressed world demand.
SACU receipts fell from a growth of 14 percent in 2000 to growths of 8 and 7 percent in
2001 and 2002 before rebounding back to 14 percent in 2003 not enough to immediately
restore previous levels because of the low base. Even with the recovery deficits were run as
Government implemented the Salary Review Exercise.
Monetary policy in Swaziland is therefore defecto inflation targeting due to the pegged
exchange rate monetary policy regime which helps instil fiscal restraint. This was further
demonstrated during the height of the global financial crisis where Swaziland experienced a
negative shock on SACU receipts putting the fiscus under pressure which led the Central
Bank to lend money to the Government to the tune of E680 million under the CBS order but
was prudent enough to repay the money in 6 months from the beginning of the fiscal year
following the fiscal year in which the debt was procured. The weakness of fiscal policy in
disarraying monetary policy therefore lies in the will of the fiscal authorities to reign in
expenditure during long periods of low Government revenue in particular SACU which
would otherwise spill over to the monetary sector in terms of rampant increases in money
supply and debt.
2. Review on Fiscal Performance.
5
Swaziland’s fiscal position is mainly supported by the South African Customs Union (SACU)
receipts which have average 50 percent of total government revenue since 1980. SACU
receipts fell to below 40 percent of total government revenue in the late 1980 and early
1990s and the fiscus slipping to deficits as seen below in figure 1. The competition for the
local economy intensified when firstly Namibia was liberated in 1990; followed by cessation
of hostility in Mozambique in 1992 and finally a democratic settlement in South Africa was
obtained in 1994. This resulted in the government running a deficit which was financed
through Treasury bill and bonds. Though the stabilization of South Africa in 1990s resulting
in growth rates of above 5 percent on average resulted in a stabilization of SACU receipts,
there was a slump in SACU receipts, in the same token as growth in South Africa faltered to
record a recession during the global financial crisis in 2010.
Figure 1. The Time Line for Growth and Fiscal Performance.
198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120122013
-50
0
50
100
150
200
-20
-10
0
10
20
30
40
50
SACU % Change The Fiscal Deficit as % of GDPGDP growth Change in Government Expenditure
SACU
% C
hang
e
Global Fin-ancial Crisis-low growth-low SACU growth
Asian Fin-ancial Crisis & Dot Com bubble-low growth-low SACU growth
SACU wind-fall -high oil pricesrecovered growth
high growth -low SACU growth (low base) -defcit
Boom Period
SACU recovers
Source: Central bank of Swaziland
The high economic growth rates recorded in the 80s resulted in government running a
surplus of 5.9 percent. Government expenditure increased in the 1990s as recurrent
expenditure increased buoyed by the good GDP growth emerging from the 1980s. The high
inflation rate of 20.3 percent in 1987 and 14.1 percent in 1990 contributed to the high
increases in Government expenditure. Government expenditure again increased
6
substantially in the year 2000 where the expenditure was mostly driven by millennium
projects capital expenditure. Government expenditure saw another substantial in
expenditure in 2004 when the salary review exercise was implemented. The SACU receipts
picked up reaching a peak of close to 70 percent of total government revenue in the years
2006, 2007 and 2008, before sliding, starting from 2009, down to a low of 38 percent of
Government revenue in 2010 due to the global financial crisis. As a consequence the
government ran deficits as high a deficit as 9.5 percent in 2010 financing the deficit through
treasury bills and, bonds and through money creation which was quickly reversed in 6
months to shield the monetary regime from collapsing. Swaziland Government expenditure
in general is driven by high revenues and high inflation which needs to be compensated to
keep Government operations stable. Government has no appetite to set aside funds in
periods of high government revenue because of a level of employment that has officially
been above 20 percent.
Figure 2 Government Salaries and Capital Expenditure and Revenues
19801983
19861989
19921995
19982001
20042007
20102013
0
5
10
15
20
25
30
35
40
45
Government Salaries and Wages percent of GDP as % of GDPCapital Expenditure percent of GDP as % of GDPGovernment Revenue and Grants as % of GDPGovernment Expenditure as % of GDP
perc
ent
Source: Central bank of Swaziland Quarterly Reports
This points to the fact that the government and the central bank do coordinate to shield the
monetary regime as stated by the Central Bank Order of 1994. The limit on Central bank
borrowing displays that there is an implicit limit put on fiscal policy by monetary authorities
Sargent and Wallace (1981)thus Swaziland can be viewed as a monetary dominant
monetary-fiscal policy set up even though the limits are on the high side In figure 2
government expenditure has steadily increased with increases in revenue. When
government revenue fell in 2010 and 2011 due to the financial crisis expenditure did not
7
adjust commensurate to a fall in revenue resulting in a huge deficit. The expenditure
remained high due to statutory commitment and resistance by the authorities in
implementing fiscal reforms of cutting recurrent expenditure but instead the capital
programme was cut compromising long term growth. The SACU receipts shock result in a
huge deficit as they account for a significant part of the revenue and coupled with a sticky
down ward wage bill.
In a nutshell the economy ran deficits in the early 80s after independence to include more
of the population in the Government budget. The was a short cut in economic growth as
the country experienced floods in 1984 but the increased expenditure after independence
and the location of firms in the economy as it was then a comparatively conducive
environment for investment in a politically turbulent Southern African region saw the GDP
growth rates rising to reach above 10 percent in 1990. The Asian financial crisis and the
Dotcom bubble in the late 90s and early 2000 saw the government being driven to a high
deficit as revenues fell. The oil price boom saw an upturn in SACU in 2005 only to experience
the global financial crisis in 2009, 2010 and 2011 where SACU receipts fell and recovered to
normal levels in 2012.
3. Review on Existing Legal and Institutional Developments Discouraging and Promoting Fiscal Dominance and Effective
Coordination of Monetary and Fiscal Policies.
The Government of Swaziland in the quest to promote monetary and fiscal coordination
with the interest of not impinging negatively on monetary policy in terms of excessive
credit promulgated Act No. 7 of 1994 providing for the issue of Treasury Bills and
Government Stock setting a limit of SZL/ZAR300 million. The act was amended in 2010 at the
height of the global financial crisis to a limit of 25 percent of GDP, which still is a limit
commensurate to the state of the economy, as a means to instil fiscal discipline though the
limit could be considered on the high side. Sargent and Wallace (1981) note along the lines
of the development of the limit that the demand for bonds place an upper limit on the stock
of bonds relative to the size of the economy and the creation of money on the other hand is
determined by who sets the limits between the monetary and fiscal authorities. Does the
monetary authority set by how much base money should grow or the fiscal authorities set it
8
by running any level of deficit with no ceiling on base money growth. Once the limit has
been reached the principal and interest due on the bonds already sold to mob up liquidity
and fight inflation must be finance at least in part by seignorage, requiring the creation of
additional base money which would ultimately defeat the initial disinflationary intentions of
setting the limit. Raising interest rates by the Central bank is no option under the fixed
exchange rate regime more in particular because of the resultant effects it would have on
growth. Thus setting the limits on government domestic credit by legislation would not need
the Central bank to sell bonds to fight inflation by mopping up liquidity. The Central bank
avoids a situation where it will have no option but to create more money to pay off the
principal interest on the bonds and treasury bills. The monetary authorities would have the
option of rolling over the debt till there is enough growth to absorb the excess liquidity
resultant from financing the principal interest rates on bonds through seignorage. Sargent
and Wallace’s model does not look at the options of rolling over debt as growth emerges
because they constructed a monetarist economy model.
The Central Bank of Swaziland Order of 1974 addresses the limits to money creation as
means to finance government deficits. The order states that the extension of credit to
government by the central bank shall at no time exceed twenty percent of the average
annual revenue of Government. The Order further states that from time to time
Government in respect of temporary deficiencies of current budget revenue, subject to
repayment within six months following the end of the financial year of the Bank in which
they are granted can borrow from the Central Bank. When calculating 20 percent of
Government revenue in 2013 the limit runs up to slightly above E1.8 billion which is above
the board, more in particular because Swaziland is in a fixed exchange rate regime. Krugman
(1979) observe that when government is no longer able to defend a fixed parity because of
the constraint on its actions, there will be a “crisis” in the balance of payments. Though
there is a constraint set by the order on Government seignorage financing it is deemed to be
on the high side because an increase of E1.8 billion in base money for the year 2013 for
example would translate into an increase of over 250 percent, which would obviously not
auger well for monetary policy and the economy at large.
The Governor in consultation with management and the monetary policy coordination
committee decides on interest rates. The monetary policy coordination committee is chaired
9
by the governor with part of bank management and stakeholders’ representatives from
outside the bank as members..
4. Challenges of Existing Fiscal Policy
The most pressing challenge for fiscal policy in Swaziland is the fact that on average with
data from 1980 to 2013 revenue from SACU has been above 50 percent of total revenue.
The fears were confirmed in 2010 during the height of the global financial crisis where
Swaziland experienced a negative shock in SACU receipts to the tune of 49 percent resulting
in a deficit of close to 10 percent. The government has been working on diversifying the
sources of revenue and has also restructured the collection of taxes by forming the
Swaziland Revenue Authority (SRA) and introducing value added tax in 2012.
The highest wage bills in sub-Sahara Africa at 15 percent of GDP on average from 2006-2010
and rising to 17.5 percent of GDP in 2014’is Swaziland’s other fiscal challenge. Olivier
Basdevant (2012) observed that the risk of a significant loss of SACU revenue calls for fiscal
reforms. Wage bills have become a frequent element of conditionality in IMF programs
with 17 out of 42 Poverty Reduction and Growth Facility (PRGF) programs (13 of 24 PRGF
programs in Africa) from 2003-2005 alluding to some form of wage bill ceiling. Basdevant
further notes that Botswana, Lesotho, Namibia and Swaziland shock on SACU receipts may
emanate from at least three structural factors: (i) a slowdown in global economic activity,
which would affect the SACU revenue pool; (ii) a reduction in the common external tariff
rates as a result of trade liberalization; (iii) the creation of the South African Development
Community (SADC) customs union and the fourth not mentioned in the paper would be the
loss of fiscal autonomy as SACU creates a development fund. Basdevant probably does not
mention this because it can swing either way depending on how each countries campaigns
for a share in the development fund which would be established by SACU.
The underlying bloated recurrent and non-discretionary expenditure, notably on the wage
bill, do not auger well in the event of a government revenue shock, which would highly likely
emanate from SACU receipts. There is therefore an urgent challenge to lower the wage bill
so that the budget can absorb shocks so as to avoid dire economic consequences coupled
with revenue diversification.
10
5. Key Features of the Operational Framework for Fiscal policy,
Monetary Policy and interaction between Fiscal and Monetary
Policy.
Paul Hilbers (2005) observes that the most important objective of central bankers is price
stability, but he further notes that there are others like economic development and growth,
exchange rate stability and safeguarding the balance of payments, and maintaining financial
stability. He notes that key variables in monetary policy include interest rates, money, credit
supply, and the exchange rate. Monetary and fiscal policies implementation by
independent of each other monetary authorities and fiscal authorities respectively is in itself
far from being independent. The implementation of one influences the other. It is therefore
essential that a consistent monetary-fiscal policy mix be pursued.
Hilbers in his 2005 paper presented at the IMF Seminar on Current Developments in
Monetary and Financial Law highlights both direct and indirect channels through which
fiscal policy affects monetary policy. Figure 1below aids the expression of both indirect and
direct channels of fiscal policy in affecting monetary policy.
(a) Excessive fiscal deficits may tempt government to finance the deficit by printing
thus leading to expansionary monetary policy which fuels inflationary pressures
and leads to a real depreciation of the currency. Depreciation of the currency
may lead to balance of payments crises.
(b) Even when deficits are not financed by money creation there is the concern of
the private sector being crowded out of the credit market by government
through high interest rates and outright non availability of credit funds for the
private sector, compromising economic growth and development in the process.
(c) Too much dependence on foreign funding of the deficit could result in exchange
rate and/or balance of payments crisis which would be worrying for the Central
bank as it also has direct implications for the maintenance of a healthy level of
reserves.
(d) A more direct way fiscal policy can affect Central bankers is by raising revenue
through increases or imposition of indirect taxes where a once off increase can
11
lead to a wage-spiral, depending on labour market forces, leading to
permanently high inflation and inflationary expectations.
(e) Confidence in the economy is compromised by large and persistent deficits which
may lead to the collapse of the monetary regime as noted by Makoto Richard
and Ndedzu Desmond (2012) in the case of Zimbabwe.
(f) Forward looking agents raise savings and reduce consumption in the light of
perceived unsustainably fiscal deficits leading to contractions in the economy
and ineffectiveness of expansionary monetary policy in resuscitating the
economy. This could be viewed as a fiscally induced liquidity trap which can be
reversed by changes in the nature of government expenditure.Fiscal
restructuring in general could help restore confidence in the economy and
resuscitate private spending.
(g) Expansionary fiscal policy will affect the Central bank whether the Central Bank is
independent or not. As expansionary fiscal policy may lead to inflation and as a
result Central banks hike interest rates straining the economy, attracting hot
money and increasing currency risks. Sterilization becomes costly for the Central
bank leading to inflationary pressures and at a later stage the reversal of the hot
money due to external factors. This may ultimately lead to a depreciation of the
currency inviting more inflationary pressures. Turkey in 1994 and 2001 and
Mexico in 1994 are relevant cases.
(h) The desire to develop financial markets with the aim of achieving economic
growth and development, funding deficits and debts and proper management of
liquidity with more flexible interest rates also results in the interaction of
monetary and fiscal policies.
The adoption of fiscal rules play an important role in avoiding large and persistent deficits
which may affect monetary policy, through variables like inflation, interest rates and
balance of payments. Transparency in monetary and fiscal policies is also vital in ensuring
that monetary and fiscal policies are coordinated effectively as promoted by the
International Monetary Fund (IMF). Most importantly, the stance of the fiscal and monetary
policy mix and the magnitude of the effect of fiscal policy on monetary policy are
determined by the nature of dominance between fiscal and monetary policy. A
12
fiscal/monetary dominant set up as espoused by Thomas J. Sargent and Neil Wallace (2012)
is as follows;
(a) where a fiscal dominant scenario exists the monetary authorities face the constraints
imposed by demand for government bonds and there is a great tendency for fiscal
authorities to run deficits that monetary authorities will be unable to control either
through the growth rate of base money or inflation forever. As put by Sargent and
Wallace (2012) the monetary authority’s inability to control inflation permanently
under these circumstances follows from the arithmetic’s of constraints it faces
emanating from how the deficit is financed.
(b) where a monetary dominant scenario exist , the fiscal authorities then face the
constraints imposed by demand for bonds, since it must set its budget so that any
deficits can be financed by a combination of seignorage chosen by the monetary
authority and bond sales to the public. Under this scenario monetary authorities can
permanently control inflation.
Therefore the severity of the effects that the fiscal deficit could have on monetary policy is
basically determined by whether a fiscally or monetary dominant scenario is obtained.
Under a fiscally dominant scenario the effect on monetary policy are detrimental to the
economy and pronounced, Makoto Richard and Ndedzu Desmond (2012) and Jean-Claude
Nachega (2005) found this to hold for Zimbabwe and the Democratic Republic of the Congo
(DRC) respectively.
The framework therefore outlines the different possible conduits through which fiscal policy
can affect monetary policy. The possibilities it should be stated are not exhaustive but are
those that have been cited in literature. They are not rigid but vary from economy to
economy depending on the disposition of monetary and fiscal policy and the underlying
economic and political dynamics. Ndezu and Makoto (2012) and Jean-Claude Nachega
(2005) found that the deficits and their monetisation were brought about by political
dynamics.
13
Figure 2 Organogram Frame work for Fiscal and Monetary Policy Interaction
14
Fisc
al D
efici
t
finance through domestic credit
market
high interest rates lead to;- crowinding out of the private
sector and weak economic growth-attract hot money and risks of
reversal of hot mney and balance of payments crisis. The prevailing
high interest rates though with more capital into the ecomnony still exclude the private sector
-ricadian equivalence
development of financial markets
proper management of
liquidity
economic growth
financed through monetisation
-inflationary pressures-balance payments
crsis/currency crisisweak economic growth
ricadian equivalence
indirect tax impostion wage-spiral and permanetly high inflation
financed through international credit market
balance of payments crisis/currency crisisricadian equivalence
Transparency
Fiscal Rules
Fiscal/Monetary Dominance
6. LITERATURE SURVEY
6.1 Theoretical and Empirical Literature on the different
Channels through which Fiscal Policy can affect Monetary
Policy.
6.2.1 The Impact of Fiscal Policy on Monetary Policy.
The relationship between budget deficits and inflation has been investigated extensively for
both industrial and developing countries with mixed results. The debate on the desirability
of deficits came to the fore front and developed primarily into two camps during the epoch
of the Great Depression with eminent scholars like Keynes stirring the debate. Deficits got
extensive attention during the period between the Great Depression in the 1930s and post-
World War II in the 1950s. John Maynard Keynes (1936) popularised the need for
governments to run deficits during recessions to compensate for the shortfall in aggregate
demand but should run surpluses in boom times so that there is no net deficit over an
economic cycle. Keynesian approach is therefore not likely to be inflationary owing to the
fiscal discipline entrenched in Keynesian thought though Keynes appreciates the advantages
of monetization at least to a certain limit. Fiscal discipline is defined as the capacity of
government to maintain smooth financial operation and long-term fiscal health; it branches
into (1) multiyear perspective on budgeting and (2) mechanism to maintain fiscal health and
stability over business cycles Yilin Hou (2003). An inflationary deficit cannot be seen to bring
fiscal discipline as resultant inflation would lead to higher real fiscal deficits according to
Aghevil and Khan (1977). The neo-classicalist on the other hand, the Chicago school of
economic and Australian school of economics among others believe deficits are a bad thing
in that they are inflationary. They argue that this is because governments pay off debts by
printing money, increasing the money supply and creating inflation. Jean-Claude Nachega
(2005) found that the degree of institutional structure linking budget deficits to money
creation has changed over time in that in the period from 1965 to the mid-1970s the DRC
was characterised by relative political stability, resulting in lower monetization of the deficit
15
and lower inflation. Nachega’s conclusion point to the fact that the nature of deficit
financing determines the impact of the budget deficit on inflation where monetization of
the budget deficit was found to be inflationary in the DRC compared to periods where the
budget deficit was less monetized. From Nachega’s conclusion the Fiscal Dominance
hypothesis which is central in the study of the impacts of budget deficits on monetary policy
can be appreciated.
It has often been argued that high inflation in developing countries is a result of persistently
high monetised deficit. The FD hypothesis that high inflation is a result of fiscally dominant
government with large and persistent deficits financed through money creation is found to
hold for developing countries. Apheous Ncube, Jackie Kitiibwa and Jean-Baptiste
Havugimana (2013) cite fiscal policy as one of the impediments in implementing monetary
policy in developing countries. It has also been observed in developing countries that
nonfiscal real disturbances or high inflation may lead to lower real tax revenues hence
higher real deficits rendering the deficit and money supply endogenous to the inflationary
process Nachega (2005).
Early studies confirm the FD hypothesis since Agheveli and Khan (1977). Agheveli and Khan
first showed that the growth in the money supply and inflation are linked in a two-way
relationship in Brazil, Colombia, the Dominican Republic and Thailand over the period 1961-
74. They ultimately found out that fiscal deficits play an important role in the inflationary
process, and that increases in these deficits are largely owing to the differences in lags of
government expenditures and revenues.
The extent to which government deficits/government debt affect, interest rates, money
supply, inflation and the balance of payments hence reserves and the exchange rate has
been studied by authors like Jean-Claude Nachega(2005), Ieva Sakalauskaite (2010), Michael
Kumhof ,and Douglas Laxton(2009) and lately Olivier Balanchard (2004) though in different
approaches and contexts. They invariable found that fiscal dominance or rather persistent
and high deficits led to a change in the conduct of monetary policy due to its effects on
interest rates, when interest rates respond to government debt Michael Kumhof , Ricardo
Nunes and Irina Yakadina (2008) and when interest rates make government debt more
attractive Olivier Balanchard (2004). Further studies looked at money supply and inflation
16
including Jean-Claude Nachega (2005), and money supply(monetary policy) on exchange
rate in Ieva Sakalauskaite (2010) and inflation under three central bank monetary
accommodation scenarios; excess, net and statutory central bank credit to government in
Makoto Richard and Ndedzu Desmond (2012).
Jean-Claude Nachega (2005) in his study on the Democratic Republic of the Congo (DRC)
argues that an increase in the budget deficit leads to increased seigniorage and the money
creation begets inflation. He found out that the degree of the institutional structures linking
budget deficits to money creation changed over time. The DRC experienced different epochs
defined by political changes that had varying effects on money creation. During period of
high political instability public finances put pressure on monetary policy leading to
monetization of the deficits hence higher inflation. The empirical results show a strong and
statistically significant long-run relationship between budget deficits and seignorage, and
between money creation and inflation. The long run inflationary impact of the deficit stays
its course even when the model takes into account output growth or velocity. Mokoto
Richard and Ndedzu Desmond (2012) did a study along similar lines and found government
deficits to be highly inflationary under excess Central bank credit to government in the case
of Zimbabwe. They concluded that when modelling fiscal dominance-monetary
accommodation hypothesis as a Vector Autoregressive model the inflationary worries are
not present if government settles its debt procured through money creation and if the
government restricts institutional credit.
The external balance appears as a variable of interest for the monetary authorities as it
features in the frame work for fiscal-monetary policy mix in figure 1. Foued Chihi and Michel
Normandin (2008) found that the covariance of the external and budget deficit is
numerically positive for 24 developing countries examined and is statistically significant for
almost all cases. They also found similar results from the estimated correlation between
external balance and budget deficits, and the estimated slope coefficient obtained by
regressing the external deficit on a constant and the budget deficit to ascertain causality,
which may not be concluded firmly from covariance analysis.
17
Raghbendra Jha (2007) observes that with poor credit and bond markets and downwardly
inflexible fiscal expenditures, some of the financing of the resultant deficit spills over onto
the external sector and the central bank.
From the manipulation of the national accounts two gap model one can deduce that fiscal
policy has indeed an impact on the external position where;
Y= C+ G+I+X-M --------------------------------(i)
Y-C = G+I+X-M
Y- C -G = S
S=I+X-M
S-I = X-M-----------------------------------------(ii)
Thus if government saving fall S will fall and it will be reflected in the external account,
more so if the reduction in savings are a result of a draw down in foreign reserves.
Therefore a government deficit is likely to have a negative impact on the external sector.
Olivier Blanchard (2004) looks at fiscal dominance when fiscal dominance has been
instigated by increased interest rates that make government debt more attractive. He
argues that if increased government debt due to increased interest rates increases the
probability of default on the debt then this may lead to a real depreciation in the exchange
rate and higher inflation. Therefore Olivier concluded that this could have dire
consequences for inflation targeting in Brazil. He ultimately concluded for Brazil in 2002 and
2003 that an increase in the real interest rate in response to higher inflation leads to a real
depreciation and the real depreciation leads in turn to further increases in inflation. Olivier
presents a model between the interest rate, the exchange rate, and the probability of
default, in a high-debt high-risk-aversion economy such as Brazil in 2002 and 2003. Jean
Nachega (2005) noted that in the case of the DRC. Beaugrand (1997) and Akitoby (2004)
estimated the fiscal deficit-inflation relationship in a single equation and failed to account
for potential feedback from inflation which is taken care of by Nachega (2005) by using a
VAR with inflation feedback effect which Blanchard (2004) explained through increased
interest as responding to deficit driven high inflation. As the interest rates respond to
18
inflationary pressures the deficit increases all the more with high interest rates as
government debt is made more attractive leading to a vicious cycle.
Sargent and Wallace (1981) present a dynamic analytical mathematical framework showing
the interactions that shows that even in an economy that satisfies monetarist assumptions 2,
if monetary policy is interpreted as open market operations then monetary policy cannot
permanently control inflation. The extent to which inflation can be controlled depends on
the way fiscal and monetary policies are coordinated. Sargent and Wallace consider two
extreme forms of coordination; one where monetary policy dominates fiscal policy by
independently setting policy such as announcing growth rates for base money for the
current period and all future periods. The monetary authorities therefore determine the
amount of revenue it will finance the government deficit through seigniorage. The fiscal
authorities are then left with the bond financing ceiling imposed by the appetite for
government bonds of the public. Under this coordination the monetary authorities can
control inflation permanently because it can choose the growth of the money base which is
closely linked to inflation under the monetarist assumptions.
But in an instance where fiscal policy dominates monetary policy; that is; the fiscal authority
independently sets its budget, announcing all future deficits such that they are determined
by how much they are going to finance the deficit from either sale of government bonds or
seignorage. Sargent and Wallace observe that under this set up the monetary authorities
face the constraints imposed by the demand for government bonds, for it must try to
finance with seignorage any discrepancy between the revenue demanded by the fiscal
authorities and the amount of bonds that can be sold to the public. Under such
circumstances there is highly likely to be runaway inflation as monetary authorities are
helpless in controlling inflation with monetary policy being ultimately determined by fiscal
policy.
6.2.2 Fiscal Policy Impact on Monetary Policy in Fixed Exchange Rate Regimes
Under a fixed exchange rate regime fiscal policy tends to be restrictive and therefore
eliminates the money creation route of deficit financing. By 1973, most major world 2 The monetarist assumptions of a monetary economy are that: the monetary base is closely connected to the price level, and the monetary authorities can raise seignorage, which is revenue raised through money creation.
19
economies had migrated to freely floating exchange rates against the dollar. The transition
to freely floating exchange rates was characterized by plummeting stock prices, skyrocketing
oil prices, bank failures and inflation. Buchanan and Wagner (1977) argued that deficits are
inflationary under an interest-rate pegging monetary policy for industrialised countries. This
is due to the non-flexibility of the interest rates where the Central bank in the desire to hold
down interest rates and thus peg at a low point under prevailing economic conditions
purchase government bonds and in the process increase base money.
The smaller economies continued with the pegged exchange rate system for various reasons
including the size and openness of their economies to trade and financial flows, the
structure of production and exports, stages of its financial development, its inflationary
history, and the nature of the shocks they faced. Industrialized economies adopted explicit
inflation targeting, along with floating exchange rate systems in the 1990s and the policy has
spread to emerging and developing economies and the industralised economies have
moved further to an ultimate fixed exchange rate system under a monetary union.
Ieva Sakalauskaite (2010) found empirical evidence to the fact of inferior public balances in
countries operating under currency pegs, and the argument that the changes in economic
conditions after a fixed exchange rate regime is established may create expansionary
temptations for politicians, resulting in lower surpluses. Ieva (2010) in his research though
finds that rigid exchange rate arrangements are conducive to delivering fiscal discipline,
while the effects of currency boards do not differ significantly from those of regular pegs. As
earlier pointed out developing countries opted for fixed exchange rate regimes for many
reasons one of which was historically high inflation figures which were to be tamed by fixed
exchange rate regimes in instilling fiscal discipline and importing low inflation. Typical the
country/currency chosen to be pegged to ought to exercise both fiscal and monetary
discipline hence low and stable inflation.
Swaziland has been on a fixed exchange rate system since 1974 on the establishment of the
Central Bank and issuance of the first notes and coins of the local currency, the Lilangeni.
Swaziland is a small landlocked country with an open economy and South Africa is her
dominant trading partner, and has a low level of financial development such as the low level
of activity in the stock exchange which factors have contributed to Swaziland adopting a
20
fixed exchange rate system. Countries with open unbiased economies and developed
financial markets as earlier observed opted for floating exchange rate systems.
Swaziland’s membership to the Common Monetary Area agreement cements the
commitment by Swaziland to exercise monetary and fiscal disciplines.
Ieva Sakalauskaite (2010) observed for 10 Central and Eastern European transition countries
over the period 1992-2008 that rigid exchange rate arrangements are conducive to
delivering fiscal discipline, while the effects of currency boards do not differ significantly.
The first generation model following the seminal work of Paul Krugman (1979) and Maurice Obstfeld (1986). A standard first-generation model of a small open economy in log notations
m-p = -α(i)…………………………..(1) Money Market Equilibrium
m = d + r………………………….. (2) Money Supply
p = p* + e………………………... (3) Purchasing Power Parity
i = i* +é…………………………. (4) Uncovered Interest Rate Parity
i:domestic-currency interest rate i*:foreign-currency interest rate
p:domestic price level p*:foreign price level
e:nominal exchange rate é:expected and actual rate of exchange rate change
r:international reserves m:domestic supply of money
m-p:real money balances
Equation 1 can be transformed to analyse the economic mechanism of inflationary effects
when discount rate in Swaziland is not equated to the discount rate in South Africa.
Substituting from equation 4 to equation 1;
i-i*=é………………………………………4(a)
m-p = -α(i*+é)……………………………..(5)
21
Equation 6: when domestic currency interest rate greater that foreign currency interest rate (i>i*)
m-p = -αi* – α(+é)……………………………(6)
Substituting from equation 6 equation 2;
d+r-p = -αi* -α(+é)…………………………...6(a)
Not equating (higher domestic currency inflation)Swaziland’s discount rates to South
Africa’s results in the expected and actual rate of exchange rate change ( αé ) in equation 6
featuring in the determination of domestic prices with expected and actual rate of exchange
rate change negatively related to real money balances. Equation 6 and 6(a) show a scenario
where domestic currency interest rate is greater than foreign currency interest rate. As é
increases (expected or actual depreciation) the domestic price level increases and real
money balances (m-p) fall. The model is built such that a forward puzzle does not exist,
where increased interest rates result in an appreciation of the exchange rate. Higher
domestic currency interest rates lead a fall in domestic credit and to an expected or actual
depreciation pushing up domestic prices and leading to a fall in real money balances. A
sustained higher domestic currency interest rate thus ultimately leads to falling output a
depreciation of the local unit resulting in higher imported inflation. Higher inflation
increases the real deficit and the government financing requirement.
Equation 6(a): when domestic currency interest rates lower than foreign currency interest rate (i<i*)
m-p =-αi*-α(-é)……………………………….6(b)
Equation 6(b) shows a scenario where domestic currency interest rate is lower than the
foreign currency interest rate. Lower domestic interest rates lead to an expected or actual
depreciation in the exchange rate putting upward pressure on domestic prices and a fall in
real money balances. Fiscal deficit would increase as the exchange rate appreciates
reducing. This as an instance where the forward puzzle will hold for Swaziland as lower
interest rates are feared to instigate capital outflows putting pressure on international
reserves mounting and the fixed exchange rate regime as reserves are meant to upkeep the
local unit. Therefore, equating the domestic interest rate to that of South Africa helps
ameliorate price instability and risks to currency attacks.
22
6.2.2.1 Economic Mechanical Operation of the Fixed Exchange rate Regime.
Substituting from Equation 2, 3 and 4 into Equation 1:
d+r-p*-é = -α(i*+é)…………………………………. (7)
When the exchange rate is fixed at e=é it follows that é =0 and with p* and i* exogenous the
equation below will only adjust via international reserves( r) and domestic credit (d) to
maintain the fixed exchange rate. If r and d do not adjust to compensate for each other the
fixed exchange rate will be under pressure to collapse. In other words money supply should
be fixed and any observed increase in money supply should be backed by economic growth.
d+r-p*-é = -α(i*)……………………………………(8)
The only way to keep the exchange rate fixed is to keep money supply fixed by adjusting
either domestic credit (d) subject to the available level of international reserves. Removing
the exogenous variables p*, é and i* the following equation is obtained:
r=-d……………………………………………… 9(a)
d=-r……………………………………………...9(b)
Equation 9 implies that domestic credit and international reserves should adjust
interchangeable to keep the money stock fixed. An increase in the central bank’s domestic
assets must be offset by decrease in foreign assets according to the mathematical
relationship in equation 9. In simpler terms, when international reserves (r) decrease it
follows that domestic credit should fall to balance the system and maintain the peg.
Krugman (1979) observed that a standard crisis occurs in something like the following
manner. A country will have a pegged exchange rate; for simplicity assume that pegging is
done solely through direct intervention in the foreign market. At that exchange rate the
government’s reserves gradually decline. A parallel scenario is when the monetary
authorities do not intervene in the foreign exchange market but use the foreign currency as
legal tender alongside the local currency. Strong trade and close proximity to the country
pegged to supports the growth of foreign assets to buoy the peg and trade links will further
be strengthened by stable domestic economic condition that would not encourage strong
trade. Should there be instability that would increase the demand for foreign assets hence
23
capital flight. The government or rather the economy not being able to keep up with the
demand for foreign assets(capital flight) would resort to a loan to sustain the import
appetite in the process increase the deficit which would impact on the conduct of monetary
policy. Hence the government’s need to implement restrictive fiscal policy and ensure
adequate economic growth under a fixed exchange rate system to limit excessive money
creation which may be undesirable for the fixed exchange rate regime. This then brings the
monetary and fiscal authorities to desire to limit money creation subject to the constraint
imposed by the fixed exchange regime, which both parties need to maintain the peg. The
krugman and Obstfeld model first generation model demonstrate why fiscal authorities
would be encouraged to exercise fiscal restraint under a fixed exchange rate regime where
currency not backed by reserves would develop a breeding ground for a currency attack.
Fiscal authorities can therefore not set the deficit without taking in account the targets set
by monetary authorities of increases in base money and level of reserves that are
commensurate to the fixed exchange rate. Under a fixed exchange regime the monetary
policy has to dominate fiscal policy for the exchange rate regime to survive. Moreover there
is no desire for the monetary authorities to abandon interest rate tracking which is in line
with maintaining the peg. Abandoning interest rate tracking under a fixed exchange rate
regime is detrimental to the economy as explained in the scenarios of above and below
target interest rates.
Doose Toublaboe and Rory Terry (2013) observe that the inflation performance is generally
better under pegged regimes with annual inflation rate of 8 percent compared with 14
percent for intermediate regimes, and 16 percent for countries with floating regimes. An
even more compelling situation according to the authors is the evidence from the CFA franc
zone. According to Reinhart and Rogoff (2002) these countries, despite the relatively high
degree of exchange rate stability they enjoyed, constitute the region of the world that has
experienced by far the most frequent bouts of deflation- about 28 percent of the time(on
average ) for the period 1970-2001. A pegged regime by providing a clear and transparent
nominal anchor has the role of communicating to the public the monetary authorities’
commitment to prudent and sustained monetary policy and low inflation target according to
the authors. Loungany and Swagel (2001) with data from 1964 to 1998 for 53 developing
countries found that while inertia factors dominate the inflation process in developing
24
countries with fixed exchange rate regimes, monetary growth and exchange rate changes
are far more important in countries with floating exchange rate regimes.
7. METHODOLOGY AND DATA
The primary purpose of the study is to investigate the effects of fiscal policy on the conduct
of monetary policy by considering the impact of the budget deficit on monetary policy
variables such as inflation and the interest rates. The long and short run behavior of inflation
and interest rate differential is investigated by estimating a model in levels and an error-
correction model in a vector autoregressive (VAR) model to address the impact of the fiscal
deficit on monetary policy variables and issues of potential feedback from inflation and
nonfiscal real disturbances to the fiscal deficit. Subsample results are also run to determine
parameter constancy and time invariance under different economic conditions. Jean-Claude
Nachega (2005) estimated for the DRC the interplay between fiscal deficits and money
creation for two sample periods; first from 1981 to 1990, when inflation was relatively high,
with low but positive output growth and limited democratization and a period of relative
stability from 1991 to 2003. The Lucas critique (Lucas 1976) dismisses the stability of
parameters given changes in the economic environment and Swaziland is no exception
given that from 1980 to 1999 the inflation rate averaged 11.8 percent and from 2000 to
2003 inflation averaged 7.2 percent. Inflation moved from double digit to single digit. The
empirical investigation will start with the investigation of time-series properties of the data
to avoid spurious regression problems that may arise when statistical inferences are drawn
from non-stationary time series data. The empirical investigation then employs the
Johansen (1988) and Johansen and Juselius (1990) multivariate cointegration procedure to
determine empirically the number of cointegration vectors, the values of the adjustment
parameters, and the exogeneity status of each variable in the model.
When estimating a model that includes time series variables, the first thing to do is to make
sure that all the time series in the model are stationary or they are cointegrated which
means the model defines a long-run relationship among the cointegrated variables. The
cointegration test generally takes two steps. The first step is to conduct a unit root test on
each variable to determine the order of integration. If they are integrated of the same
order, the second step is to estimate the model and test whether the residual of the model
25
is stationary. The techniques that will be used as earlier mentioned is the Johansen
cointegration test in EViews and also estimate the error correction model using the Vector
Autoregression method. The data used in the estimation are sourced mainly from the
Central Bank of Swaziland quarterly publications and Central Statistics Office (CSO) data. The
variables to be included in the model from the strength of the literature and theoretical
review and research question are the GDP growth(gdpgr), the exchange rate(exrate), gross
international reserves(gir), the deficit as a percent of GDP(df), discount rate(dr), base
money(mb) and money supply (m1) and inflation (infl). A single equation model is
estimated to investigate the impact of the financial crisis and introduction of value added
tax on monetary policy. The equation to determine the precise direction of monetary policy
in Swaziland since Swaziland is in a Common Monetary Area and pursues a fixed exchange
rate regime is to run the fiscal variable (deficit) against the interest rate differential between
South Africa and Swaziland. Swaziland by virtue of pursuing a fixed exchange rate regime
tracks (to guard against capital flight detrimental to the pegged exchange rate regime)
South Africa’s discount rate with flexibility so as to sometimes respond to variables like
credit extension, GDP growth and inflation. . The impacts of the deficit and GDP growth on
the discount rate differential between South Africa and Swaziland is investigated using the
Johannes approach.
8. ECONOMETRIC RESULTS8.1 Unit-Root Tests
The first step of testing cointegration is to test all the time series variables for stationarity by
conducting the augmented Dickey-Fuller unit root test on each of the 8 series. The null
hypothesis is the presence of a unit root. An important practical issue for the
implementation of the ADF test is the specification of the lag length. If the lag length is too
small then the remaining serial correlation in the error will bias the test and if it is too large
then the power of the test will suffer. The lag length in the ADF regression is selected using
the Akaike information criterion. Venus Khim-Sen Liew (2004) found that the Akaike
information criterion is superior to the other criteria under study in the case of small sample
(60 observation and below), in the manner that it minimizes the chance of under estimation
while maximizing the chance of recovering the true lag length. If the absolute value of the t-
26
statistic for testing the significance of the last lagged difference is greater than 1.6 then set
the lag length there and perform unit root test. Otherwise reduce the lag length by one and
repeat the process. In the unit root test of levels the intercept and also time trend is
included. In the unit root test of first differences only the intercept without trend is
included.
Table 1. ADF Statistics for Testing for a Unit Root
Variables t-adf Lag Additional regressors
In Levels1. gir(gross international reserves)
-1.35 2 Constant +trend
2. exrate(exchange rate E/USD) -0.71 1 Constant + trend3. m1(money supply) 2.10 7 Constant + trend4. mb(base money)5. df(deficit as a percent of GDP)6. dr(discount rate)7. infl(inflation)8. SA inflation9. Deficit10. discount rate diff11. GDP growth12. Dummy VAT13. Dummy FCIn differences
3.18-3.83-3.28-0.17-1.51-3.83-1.86-2.04
--
83065884
Constant + trendConstant + trendConstant + trendConstant + trendConstant + trendConstant + trendConstant + trendConstant + trend
--
1. gir(gross international reserves)
-6.35*** 1 Constant
2. exrate(exchange rate E/USD) -4.54*** 1 Constant3. m1(money supply) 5.80*** 6 Constant4. mb(base money)5. df(deficit as a percent of GDP)6. dr(discount rate)7. SA inflation8. infl(inflation)9. discount rate diff10. GDP growth11. Inflation
2.97*-4.41***-7.43***-5.11***-5.64***5.68***
-3.62**-6.40***
1040
125
1031
ConstantConstantConstantConstantConstantConstantConstantConstant
The Augmented Dicky-fuller test finds that all variables are non-stationary at levels meaning
that they have a long run. The Johansen cointegration test in E-views and the error
correction model using the VAR is then employed to determine the long run and short run
relationship respectively.
27
8.2 THE LONG RUN INFLATION ESTIMATION EQUATION AND DIAGNOSTIC TESTS.
The Johansen procedure is applied to the non-stationary series, the seigniorage, money
supply, gross international reserves, inflation, discount rate, exchange rate and the deficit to
test for cointegration. The cointegration of the variables suggests that there is a long-run
relationship among the variables. Ramesh (2000) used the Johansen test uses the VAR
method, in which all cointegrating series are considered endogenous. The likelihood ratio
(LR) value is greater than the 5 percent critical value where it gives 3 cointegrating equation.
The trace statistics therefore shows three cointegrating equations at the 0.05 level. The
Max-egen value test indicates 2 cointegrating equation at the 0.05 level. The first
cointegrating equation is taken and it gives credence to economic sense. The normalised
coefficient table giving the estimate of the model (cointegrating equation) with all variables
are taken to the left side to change signs, hence equation 1 see appendix1. The models is
found to be unstable where upon variables suspected to be multicollinear are removed from
the model to get the following long run estimation.
EQUATION 8(i).
Johansen Cointgration E-views Output Log run
CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB CLSAINFL 1.000000 -0.256780 -0.344805 0.813924 0.016732 -0.076751 0.844468 -0.187731
(0.12617) (0.09316) (0.07099) (0.00795) (0.20131) (0.31249) (0.08880)
Table 2. Lag length CriterionVAR Lag Order Selection CriteriaEndogenous variables: CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMBExogenous variables: CDate: 11/12/15 Time: 09:12Sample: 1981 2013Included observations: 31
Lag LogL LR FPE AIC SC HQ
0 -142.5710 NA 0.000586 9.585228 9.862774 9.6757011 -17.11875 194.2487 1.92e-06 3.814113 5.756935* 4.4474242 35.73076 61.37363* 8.57e-07* 2.727048* 6.335145 3.903197*
28
* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion
Figure3. AR ROOT Stability Tests
Money supply is and gross international reserves are dropped from the estimation to render
the model stable and the following results are obtained from the Johannes Cointegration
estimations.
Table 3. Johansen Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No of CE(s)
Eigen value Trace
Statistics
0.05
Critical Value
Prob.**
None* 0.917752 164.9246 95.75366 0.0000
At most 1* 0.719539 89.98402 69.81889 0.0006
At most 2* 0.557740 51.84440 47.85613 0.0201
At most 3 0.423246 27.36865 29.79707 0.2203Trace test indicates 3 cointegrating eqn(s) at the 0.05 level*denotes rejection of the hypothesis at the 0.05 level**MacKinnon-Haug-Michelis (1999) p-values
29
The likelihood ratio (LR) value is greater than the 5 percent critical value where it gives 3
cointegrating equation. The trace statistics therefore shows three cointegrating equations at
the 0.05 level. The Max-egen value test indicates 2 cointegrating equation at the 0.05 level.
Table 4 Johansen Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hyphothesis No. of CE(s)
Eigenvalue Max-Eigen Statistics
0.05Critical Value
Prob.**
None* 0.917752 74.94055 40.07757 0.0000
At most 1* 0.719539 38.13963 33.87687 0.0146
At most 2 0.557740 24.47575 27.58434 0.1190
At most 3 0.423246 16.51016 21.13162 0.1964Max-eigenvalue test indicate 2 cointegrating eqn(s) at the 0.05 level*denotes rejection of the hypothesis at the 0.05 levelMacKinnon-Haug-Michelis (1999) p-values
Inflation= -0.128*Fiscal balance + 0.395*LnSAinflation (0.0116) (0.1061) (11.044) (3.723)
-0.378*LnDR + 0.388*LnExrate - 0.337*LnMb-------------------Eqn...8(ii) (0.1475) (0.125) (0.087) (2.5627) (3.104) (3.874)
R2 = 0.837
Adj. R-sq = 0.729
F-statistics = 67.384
The figures in parenthesis are standard errors and all variables are found to be highly significant with t values greater than 2.
30
DIAGNOSTIC TESTS
Table 5 Diagnostic Test for the Inflation Equation.
Diagnostic Tests H0 Jarque-Bera Probability
Normality Residual is normally
distributed
143.6835 0.9836
Dependent variable Chi-sq Probability
Exogeniety Inflation 34.44540 0.0002
H0 Q-statistics Probability
LM Serial Correlation No serial correlation 33.3333 0.5961
H0 Chi-sq Probability
White
Heteroskedasticity(No
Cross Term)
No heteroskedasticity 507.2970 0.4504
Akaike information
Criterion
-2.72705 lag order 2 chosen by akaike information criterion
Schwarz 6.335145
Stability Tests
Roots of Characteristic PolynomialEndogenous variables: CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMBExogenous variables: CLag specification: 1 2Date: 11/11/15 Time: 15:54
Root Modulus
0.970610 - 0.027202i 0.970992 0.970610 + 0.027202i 0.970992 0.565203 - 0.621910i 0.840373 0.565203 + 0.621910i 0.840373-0.630287 0.630287-0.150617 - 0.597776i 0.616459-0.150617 + 0.597776i 0.616459-0.272598 - 0.528869i 0.594989-0.272598 + 0.528869i 0.594989 0.438361 - 0.286136i 0.523483 0.438361 + 0.286136i 0.523483-0.080844 0.080844
No root lies outside the unit circle. VAR satisfies the stability condition.
31
Figure 4. AR Root Stability Test Inflation Equation 8(ii)
The model satisfies the stability condition.
Results Interpretation
A percentage in increase in the fiscal balance leads to a fall in inflation in the long run by
0.128 percent and a percentage increase in the exchange rate leads to a 0.388 percent rise
in inflation in the long run. The monetary base due to the fixed exchange rate regime and
the prevalent high states of liquidity tends to suppress inflation in the long run. This
inefficacy of monetary policy in a fixed exchange rate regime is cited in the literature review
by Loungany and Swagel (2001) in his study of 53 developing countries. The study by
Magnus Saxegaard(2006) also reinforces Loungany and Swagel (2001) for countries in high
liquidity like Swaziland where he found that excess liquidity weakens the monetary policy
transmission thus the ability of monetary authorities to influence demand conditions in the
economy. Base money though driven by a high fiscal deficit would have different effects on
the behaviour of inflation in the long run as it reduces liquidity.
In figure 4(i) excess liquidity is almost equivalent to base money
32
Graph 6(i). EXCESS LIQUIDITY
1990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014
0
500000
1000000
1500000
2000000
2500000
Swaziland
Base Money Excess liquidity
000
Graph 6(ii) Demonstration of Liquidity Pressures on Base Money
19811983
19851987
19891991
19931995
19971999
20012003
20052007
20092011
2013
-2500000
-2000000
-1500000
-1000000
-500000
0
500000
1000000
0
5
10
15
20
25
Mb less excess liquidity MbInflation
SOURCE:CBS QUARTERLTY REVIEW
The liquidity pressures have the tendency of dissipating the effects of base money on
inflation in the long run as shown in graph 4(ii) above. The increase in liquidity
reduces the velocity of money or rather it increases base money that is not for
transaction, precautionary or speculative purposes.
33
Graph 7 IMPULES RESPONSE FUNCTION and VARIANCE DECOMPOSITION OF THE LOG RUN EQUATION
34
Results Interpretation.
The inflation rate responds by rising and petering out in the tenth period to a negative shock
in the fiscal position. Inflation responds by falling to a positive shock in the discount rate
and it responds by increasing to a shock in the exchange rate to stabilise in the tenth period.
The discount rate responds by falling and then rising only to fall and stabilise in the tenth
period to a negative shock in the fiscal position. The monetary base responds by falling to a
shock in the discount rate and remains fairly stable to a deficit shock as government is not
apt to finance the deficit through money creation. The exchange rate shock lead to a rise in
the monetary base and the discount rate responds by increasing and then stabilising to a
shock in the inflation rate. The fall in the exchange rate leads to a deterioration in the fiscal
balance.
Therefore negative fiscal shocks lead higher inflation rate and the need to keep interest
rates on the high side though being mindful of the fixed exchange rate regime Swaziland is
pursuing and the negative implications high interest rates would have on growth. The
shocks in the fiscal balance may be induced by deterioration in the exchange rate position or
a fall in SACU receipts accompanied by high government expenditure. Due to high levels of
liquidity in the Swazi economy the monetary base has a muted effect on inflation as also
found by Magnus Saxegaard (2001) for countries in sub-Sahara Africa. He found that in both
the flexible and fixed exchange rate regime the price level dose not respond significantly to
unexpected changes in reserve money. Given the quantity theory of money where:
M*V=P*Q, M=money supplyV=velocityP=Price
35
Q=Real Output
Given that relationship, an increase in M might result in an increase in P or it might result in
an increase in Q or real output. Given the large amount of slack in the economy, most of it is
likely to find its way into Q not P. But due to high levels of liquidity base money gives rise to
a long run negative effect on inflation, meaning the increase in base money is not
adequately compensated with increases in velocity in an environment of involuntary excess
liquidity.
Graph 8.
198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010
0102030405060
Crude Estimation of the Trend for Velocity of Money
The velocity of money as crudely calculated by nominal GDP divided by base money shows a
dampening slope as the velocity of money in Swaziland stays less commensurate to the
increase in money supply creating involuntary excess liquidity which explains the effect of a
long term depression in prices due to increases in base money. The variance decomposition
shows that base money virtually has no effect in variance of the deficit and the discount rate
and has a huge variance effect on inflation.
8(iii) THE SHORT RUN INFLATION ESTIMATION EQUATION AND DIAGNOSTIC TESTS.
The short-run dynamics of inflation are estimated using e-views. In the Vector Error
Correction Model (VECM), short run association is deduced from the coefficient of the
differenced lagged variables. The coefficients in the cointegrating equation give the
estimated long-run relationship among the variables; the coefficient of the error term in the
VECM shows how deviations from that long-run relationship affect the changes in the
36
variable in the next period. Jean Claude Nachega (2005) yields the parsimonious SVECM by
deleting insignificant regressors.
8.3 The Short run Estimation of the Inflation Equation
Error
Correction
D(CLINFL
)
D(CLNDR) D(Cdeficit) D(CLNExrate) D(CLNMB) DCLSAINFL
CointEq1
Std Error
p-value
-0.3526
(0.3094)
(-1.1394)
0.1740
(0.1746)
(0.9967)
-0.7312
(2.4827)
(-2.9451)
0.2102
(0.1147)
(1.8319)
-0.0033
(0.0779)
(-0.0421)
0.2569
(0.2420)
(1.0616)
-Johansen Cointegration Output Short Run Estimation Equation.
Table 4 indicate that ECT in all equations has the negative and positive sign. The inflation
rate, fiscal balance and money supply are all functions of equilibrium in the cointegrating
relationship with the discount rate, South Africa inflation and base money being
disequilibrating functions. The fiscal balance, inflation and money supply have equilibrating
effects on the error term of inflation with only the fiscal balance having a significant error
correction terms. The system is stable with a sum of the coefficient of -0.446 with the fiscal
balance by its significance being critical to stabilisation of inflation.
The test for the presence of a short run relationship in the variables is done with the block
exogeniety Wald Causality Test.
Granger causality is done to see the short run causality running from independent variables
to dependent variables. It is found that the test statistics for granger test should follow the
chi-square distribution thus the results in e-views show the p value greater than 0.05.
37
Table 6(i). Block Exogeneity WALD CAUSALITY TESTS short run: Inflation Dependent Variable
Independent Variable Chi-sq. df. InflationDeficit 1.692092 2 p-value = 0.4291
Exchange rate 4.384557 2 p-value = 0.1857
Base Money 2.827884 2 p-value = 0.6279
South Africa Inflation 3.366878 2 p-value = 0.1117
Discount rate 0.930686 2 p-value = 0.2432
All 15.59706 10 0.118
Table 6(ii) Block Exogeneity WALD CAUSALITY TESTS: Fiscal balance
Dependent Variable
Independent Variable Chi-sq. df. Fiscal Balance
Inflation 8.640843 2 p-value=0.0133
South Africa Inflation 16.36501 2 p-value=0.0003
Discount Rate 5.173644 2 p-value=0.0753
Exchange rate 2.105563 2 p-value=0.3490
Monetary Base 3.196265 2 p-value=0.2023
33.60322 10 0.0002
Since all values in the table 4(i) are not significant they then all do not have a short run
relationship. Which means the deficit, exchange rate, base money, South Africa inflation and
the discount rate do not granger cause inflation in the short run or rather they are weakly
exogenous. Inflation, South Africa inflation and the discount rate granger cause the fiscal
balance in the short run through high repayments in with higher discount rates, and higher
real deficits through higher South Africa and domestic inflation in the short run.
The fiscal balance, the exchange rate, base money, South Africa inflation and discount rate
do not have a short run impact on inflation. Inflation, South Africa inflation and the discount
rate all have short run impact on the fiscal balance.
38
8.4 THE LONG RUN DISCOUNT RATE DIFFERENTIAL ESTIMATION EQUATION AND DIAGNOSTIC TESTS.
The monetary authorities conduct monetary easing by lagging the discount rate from that of
South Africa particularly when domestic growth prospects are grim. The below vector
autoregressive model estimates the impact of the deficit and other variables to discount
rate differential from that of South Africa.
The likelihood ratio (LR) value is greater than the 5 percent critical value where it gives 2
cointegrating equations. The trace statistics show two cointegrating equations at the 0.05
level. The max-eigen value test indicates 2 cointegrating equations at the 0.05 level.
The long-run equation estimation
Discount differential =Dd
Fiscal Balance = FB
GDP growth = GDPGr
INF =Inflation
Monetary base=Mb
The variables are cantered and lodged.
Unrestricted Cointegration Rank Test (Trace)
Hypothesized No. of CE(s)
Eigen value TraceStatistics
0.05Critical Value
Prob.**
None* 0.880858 140.1625 69.81889 0.0000At most 1* 0.612328 76.33921 47.85613 0.0000At most 2* 0.529998 47.91133 29.79707 0.0002At most 3* 0.433321 25.26077 15.49471 0.0013At most 4* 0.239717 8.221919 3.841466 0.0041Trace test indicates 5 cointegrating eqn(s) at the 0.05 level*denotes rejection of the hypothesis at the 0.05 level**Mackinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
39
HypothesizedNo. of CE(s)
Eigenvalue Max-EigenStatistics
0.05Critical Value
Prob.**
None* 0.880858 63.82329 33.87687 0.0000At most 1* 0.612328 28.42788 27.58434 0.0389At most 2* 0.529998 22.65057 21.13162 0.0303At most 3* 0.433321 17.03885 14.26460 0.0177At most 4* 0.239717 8.221919 3.841466 0.0041Max-eigenvalue test indicates 5 cointegrating eqn (n) at the 0.05 level*denotes rejection of the hypothesis at the 0.05 level**Mackinnon-Haug-Michelis (1999) p-values
Dd = 0.11*FB +0.18*GDPGr –0.38*Mb+1.31*INF …….Eqn...8(iii)Std Errors (0.04) (0.08) (0.54) (0.17) t-value (2.54) (2.37) (2.45) (2.21)
DIAGNOSTIC TESTS
Table 7 DIAGNOSTIC TESTS FOR DISCOUNT DIFFERENTIAL EQUATION
Diagnostic Tests H0 Jarque-Bera ProbabilityNormality Residual is normally
distributed2.074557 0.9957
Dependent variable Chi-sq Probability
Exogeniety Discount rate differential 25.18214 0.0014
H0 Q-statistics Probability
LM Serial Correlation No serial correlation 24.29926 0.561325
H0 Chi-sq Probability
White
Heteroskedasticity(No
Cross Term)
No heteroskedasticity 284.3936 0.7328
Akaike information
Criterion
12.45074 lag order 2 chosen by akaike information criterion
Schwarz 14.99491
Table 7 Discount Differential Equation Stability Tests
Roots of Characteristic PolynomialEndogenous variables: CSASDDIFF CDEFICIT
40
CGDPGR CLNMBExogenous variables: CLag specification: 1 2Date: 11/12/15 Time: 11:09
Root Modulus
0.984472 0.984472 0.625941 - 0.447823i 0.769641 0.625941 + 0.447823i 0.769641 0.210650 - 0.591834i 0.628204 0.210650 + 0.591834i 0.628204-0.528342 0.528342-0.157332 - 0.453437i 0.479957-0.157332 + 0.453437i 0.479957
No root lies outside the unit circle. VAR satisfies the stability condition.
Graph 8 AR root Stability Test Discount Rate Differential Equation.
41
19811984
19871990
19931996
19992002
20052008
2011
-5
0
5
10
15
20
25
InflationSA less SD Discount rate
As the fiscal balance improves the discount rate differential widens and it is significant;
improvements in the fiscal balance may reduce domestic discount rate in that there is a
relief on inflationary pressures. When GDP growth rises the discount rate differential closes
representing a tightening of monetary policy and the variable is significant attesting to the
fact that monetary authorities do exercise an accommodative monetary policy with
reference to the peg when economic growth is not favourable. The monetary authorities
used interest rate difference from that of South Africa during the financial crisis as a
stimulus measure to low economic growth. An increase in the monetary base leads to a
close in the discount rate differential depicting tight monetary policy in avoidance of capital
outflows and pressure on the exchange rate peg. The monetary base is highly significant
attesting to the monetary authorities’ commitment to the fixed exchange rate.
42
Graph 10 IMPULSE RESPONSE FUNCTION Discount Differential Equation (run new innovations)
Results Interpretation
The discount rate differential between South Africa and Swaziland responds by closing down
to a shock in the monetary base to curtail capital outflows where a the monetary base is
43
rarely influenced by the fiscal deficit. The discount rate responds by falling to widen the
discount rate differential and then rising to close the differential to positive shock in the
fiscal balance. When the fiscal balance is healthy there are less inflationary pressures and
monetary authorities can afford to loosen monetary policy but should tighten towards the
tenth period.
Table 8.5 THE SHORT RUN EQUATION ON DISCOUNT RATE DIFFERENTIAL
Error
Correction
D(CSASDDIFF
)
D(CDEFICIT) D(CGDPGR) D(CLNINFL) D(CLNMB)
CointEq1 -0.0820929
(0.01467)
(-5.596)
-0.096297
(0.08521)
(-1.13742)
-0.151226
(0.046797)
(-2.46003)
0.019106
(0.09061)
(0.21087)
-0.027248
(0.01693)
(-1.60992)
The discount rate differential, the fiscal balance, GDP growth and base money are all
equilibrating functions with GDP growth and discount rate differential being significant. The
Wald statistics below confirm the error correction term results above. The GDP growth
therefore can be regarded as having a short run impact on the discount rate differential with
the support of the Wald tests and error correction term results. The monetary authorities
have a tendency of setting the domestic discount rate lower than that of South Africa when
there is exceptionally low economic growth in Swaziland.
Table 9 Block Exogeneity WALD CASUALITY TEST : Discount rate differential
Dependent VariableIndependent Variable Discount rate differentialFiscal balance p-value = 0.0584
GDP growth p-value = 0.0001
Swaziland Inflation p-value = 0.2153
Base Money p-value = 0.0047
The fiscal balance has no short run impact on interest rate differential between South African and
Swaziland but the performance of GDP and base money impact on the interest rate differential.
44
Swaziland usually keeps its discount rate lower than that of South Africa if GDP is not performing
well. Increases in base encourage a close in the differential to avoid capital flight.
8.6 THE SHORT RUN VALUE ADDED TAX AND FINANCIAL CRISIS ON INFLATION ESTIMATION EQUATION.
Due to the short time series not all variables can be run on a vector error correction model
thus a single equation is estimated to assess the impact of the financial crisis and
introduction of value added tax on inflation.
The financial crisis was preceded by a rise in inflation which saw interest rates rising and the
collapse of the housing subprime. The resultant fall in aggregate demand saw inflation fall.
The dummy variable for the financial crisis and value added tax have no cointegrating
relation with the other variables and can therefore not be included in the Johansen
procedure of estimation employed above hence their effect on inflation is investigated
through the estimation of a single equation below.
Table 10 Results of the Financial Crisis and Value Added Equation.Dependent Variable: DCLNINFMethod: Least SquaresDate: 09/14/15 Time: 09:11Sample (adjusted): 1984 2013Included observations: 30 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
DCLNM1(-2) 0.363016 0.658516 0.551264 0.5879DCLNMB(-1) 0.243682 0.679348 0.358699 0.7238
DCLNGIR 0.222979 0.322777 0.690814 0.4980DCLNEXRATE(-1) 0.226699 0.515387 0.439862 0.6650
DCLNDR(-2) -0.253979 0.358906 -0.707648 0.4878CDEFICIT -0.006445 0.020399 -0.315956 0.7555
DCLSAINFL 0.255887 0.220343 1.161314 0.2599ECT(-1) -0.937731 0.225878 -4.151489 0.0005DUMFC -0.191652 0.294911 -0.649866 0.5236
DUMVAT 0.356936 0.361863 0.986383 0.3363C -0.151810 0.138017 -1.099935 0.2851
R-squared 0.707487 Mean dependent var -0.043255Adjusted R-squared 0.553533 S.D. dependent var 0.460908S.E. of regression 0.307970 Akaike info criterion 0.758946
45
Sum squared resid 1.802065 Schwarz criterion 1.272719Log likelihood -0.384193 Hannan-Quinn criter. 0.923306F-statistic 4.595444 Durbin-Watson stat 1.768183Prob(F-statistic) 0.002121
DIAGNOSTIC TESTS
Table 10 Diagnostic Results Value Added Tax and Financial Crisis Equation.
Diagnostic Test H0 F-Statistics ProbabilitySerial Correlation No serial Correlation 0.23914 0.7899
nR2 Chi-sq
Heteroskedasticity
Breuch-Pagan-
Godfrey
No Heteroskedasticity 1% 17.59929 23.20925
No Heteroskedasticity 5% 17.59929 18.30704
H0 Jarque-Bera Probability
Normality Residual are normally distributed 0.218220 0.896632
Durbin Watson 1.82˞
The single equation has an R2 of 70 percent and a durbin Watson statistics of approximately
2. The Breusch-Pagan-Godfrey test shows that there is an absence of heteroskedasticity
since the nR2 value of 17.6 is lower than the 1% critical chi-sq value of 23.20925, even the
5% critical chi-sq value of 18.30704 we fail to reject the null hyphothesis of no
heteroskedasticity. The presence of serial correlation is also rejected with p values of
greater than 5 percent recorded. Multicollinearity is solved by centering the variables.
Figure 11 -Value Added Tax and Inflation
46
Jan-05
May-05
Sep-05
Jan-06
May-06
Sep-06
Jan-07
May-07
Sep-07
Jan-08
May-08
Sep-08
Jan-09
May-09
Sep-09
Jan-10
May-10
Sep-10
Jan-11
May-11
Sep-11
Jan-12
May-12
Sep-12
Jan-13
May-13
Sep-13
-4-202468
10121416
Swaziland-inflation Polynomial (Swaziland-inflation)South Africa-inflation Polynomial (South Africa-inflation )Series3
The government of Swaziland in 2012 introduced value added tax which did not result in
change in the trajectory of inflation but instead the deviation of local inflation from that of
South Africa was a bit pronounces. The inflation did not experience a shock that resulted
prior to the financial crisis. The value added tax resulted in a temporal upward movement
in inflation of 0.428 percent in 2012 and the financial crisis on the other hand resulted in a
fall in the inflation rate by 0.121 percent.
9. RECOMMENDATIONS AND POLICY CONCLUSIONS.
The significantly negative long run impact the fiscal balance has on inflation suggests that
persistent fiscal deficits would be inflationary. The persistently high deficits would further
result in persistently higher discount rates which would not auger well for economic growth.
A healthy fiscal balance would ease pressures on tight monetary policy and the domestic
discount rate would fall below that of South Africa. The Swaziland monetary authorities
pursue a fixed exchange rate regime where they generally track discount rate in South Africa
in their conduct of monetary policy. The authorities have a tendency of keeping the
domestic discount lower than that of South Africa when there is a pressure for an
accommodative monetary policy due weak growth. The fiscal balance which relies on South
African Custom receipts for a healthy position deteriorates to push up inflation and the
discount rate in the long run compromising economic growth prospects. Exchange rate
deterioration also worsens the fiscal balance and results in a deficit which leads to higher
47
long term inflation. The monetary authorities should therefore discourage the government
from running persistent fiscal deficits by advocating a fiscal restructuring trimming the
bloated recurrent expenditure in particular the wage bill. The implementation of an
accommodative monetary policy is difficult under a huge budget deficit and the healthier
the fiscal position the better will be the long run discount rate trajectory. Fiscal shocks
should therefore be avoided by basically reducing the recurrent budget in particular the
wage bill and diversifying sources of revenue away from heavy reliance on SACU recipients.
The heavy reliance of government revenue on SACU receipts renders the fiscal balance
susceptible to shocks.
48
APPENDIX I. DEFINITIONS AND SOURCES OF VARIABLES
1. CLNINFL – domestic inflation2. CLNGIR – gross international reserves3. CLNEXRATE – ZAR/USD exchange rate4. CLNDR – domestic discount rate 5. CDEFICIT- fiscal balance6. CLNM1-money supply7. CLNMB-base money8. CLSAINFL-South Africa Inflation9. CGDPGR-Gross Domestic Product Growth10. CSASDDDIFF-South Africa, Swaziland discount rate differential
The variables have been centred as denoted by C and logged as denoted by LN save for those in percentages.
Source is the Central Bank of Swaziland Quarterly Report
APPENDIX II. TABLES AND FIGURES
TABLE AII1. COINTEGRATING EQUATION- Johansen Test; Long run equations -FOR INFLATION. VAR estimation and Diagnostic Tests.
Date: 09/04/15 Time: 09:36Sample (adjusted): 1983 2013
Included observations: 31 after adjustmentsTrend assumption: Linear deterministic trend
Series: CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB CLSAINFLLags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05
49
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.961750 282.8591 159.5297 0.0000At most 1 * 0.916669 181.6869 125.6154 0.0000At most 2 * 0.682905 104.6541 95.75366 0.0106At most 3 0.592106 69.04890 69.81889 0.0575At most 4 0.403547 41.24969 47.85613 0.1809At most 5 0.388263 25.23030 29.79707 0.1534At most 6 0.271758 9.995273 15.49471 0.2811At most 7 0.005293 0.164514 3.841466 0.6850
Trace test indicates 3 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.961750 101.1722 52.36261 0.0000At most 1 * 0.916669 77.03283 46.23142 0.0000At most 2 0.682905 35.60516 40.07757 0.1465At most 3 0.592106 27.79921 33.87687 0.2229At most 4 0.403547 16.01939 27.58434 0.6641At most 5 0.388263 15.23502 21.13162 0.2728At most 6 0.271758 9.830759 14.26460 0.2233At most 7 0.005293 0.164514 3.841466 0.6850
Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 4.340328 -1.114510 -1.496565 3.532695 0.072622 -0.333126 3.665270 1.487177 7.745601 -6.798221 -1.074260 0.260361 4.667609 -13.14691 3.327311 -3.522847 2.199235 -1.010841 -0.470988 -5.407373 10.06923-2.404371 2.270619 -1.778983 0.221049 -0.536756 -5.415453 4.292863-1.122585 -5.672100 0.675782 5.511216 0.007094 8.641862 -2.227827 0.889281 -6.821465 -0.491653 -1.210671 -0.094868 -14.52137 25.22772 0.570783 -1.666752 4.269467 -0.892476 0.010999 4.836908 -6.562962 0.479943 0.942051 -3.414547 -1.682066 0.203953 4.575122 -4.213899
Unrestricted Adjustment Coefficients (alpha):
D(CLNINFL) -0.301636 0.002205 -0.036671 0.051236 0.031397 0.096352D(CLNGIR) -0.030826 0.082293 -0.017150 0.025527 0.028018 0.057185
D(CLNEXRATE) -0.003925 0.071600 0.005144 0.054760 -0.009524 -0.002526D(CLNDR) 0.007175 0.099560 0.018122 -0.003060 -0.069677 0.069337
D(CDEFICIT) -0.941203 2.043474 0.785595 0.378608 0.596192 0.432193D(CLNM1) -0.017025 -0.029107 0.006490 -0.019033 0.005694 -0.000741D(CLNMB) 0.009755 -0.015077 -0.021477 -0.018729 0.004271 -0.017358
D(CLSAINFL) -0.211346 0.188675 0.063868 -0.094742 -0.004277 0.047642
1 Cointegrating Equation(s): Log likelihood 121.6943
Normalized cointegrating coefficients (standard error in parentheses)
50
CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000 -0.256780 -0.344805 0.813924 0.016732 -0.076751 0.844468
(0.12617) (0.09316) (0.07099) (0.00795) (0.20131) (0.31249)
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.309199
(0.22012)D(CLNGIR) -0.133795
(0.13784)D(CLNEXRATE) -0.017035
(0.10333)D(CLNDR) 0.031140
(0.18462)D(CDEFICIT) -4.085130
(3.35421)D(CLNM1) -0.073893
(0.06234)D(CLNMB) 0.042338
(0.07046)D(CLSAINFL) -0.917313
(0.25076)
2 Cointegrating Equation(s): Log likelihood 160.2107
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000 0.000000 -0.543387 0.741740 0.024172 0.074324 0.389426
(0.06969) (0.06492) (0.00733) (0.18488) (0.21813) 0.000000 1.000000 -0.773356 -0.281109 0.028973 0.588344 -1.772109
(0.05915) (0.05511) (0.00622) (0.15693) (0.18515)
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.305920 0.353252
(0.23268) (0.39686)D(CLNGIR) -0.011411 0.671765
(0.12017) (0.20497)D(CLNEXRATE) 0.089447 0.558958
(0.08241) (0.14056)D(CLNDR) 0.179204 0.763157
(0.16778) (0.28616)D(CDEFICIT) -1.046122 16.87691
(2.89584) (4.93914)D(CLNM1) -0.117180 -0.206475
(0.05911) (0.10081)D(CLNMB) 0.019916 -0.127652
(0.07293) (0.12439)D(CLSAINFL) -0.636720 1.696946
(0.18596) (0.31717)
3 Cointegrating Equation(s): Log likelihood 178.0133
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000 0.000000 0.000000 -1.151311 -0.166164 -1.442959 1.461353
(0.41951) (0.04743) (1.19898) (1.35033) 0.000000 1.000000 0.000000 -2.975327 -0.241915 -1.571074 -0.246527
(0.62221) (0.07034) (1.77828) (2.00277) 0.000000 0.000000 1.000000 -3.483799 -0.350275 -2.792269 1.972677
(0.78829) (0.08912) (2.25294) (2.53734)
51
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.427935 0.482437 0.355783
(0.28382) (0.42976) (0.36558)D(CLNGIR) -0.068474 0.732181 -0.551030
(0.14693) (0.22247) (0.18925)D(CLNEXRATE) 0.106564 0.540835 -0.469564
(0.10160) (0.15385) (0.13087)D(CLNDR) 0.239502 0.699315 -0.647714
(0.20604) (0.31198) (0.26539)D(CDEFICIT) 1.567798 14.10938 -10.75571
(3.44271) (5.21294) (4.43442)D(CLNM1) -0.095586 -0.229339 0.237627
(0.07257) (0.10989) (0.09348)D(CLNMB) -0.051545 -0.051992 0.040666
(0.08609) (0.13035) (0.11088)D(CLSAINFL) -0.424212 1.471949 -0.825898
(0.21571) (0.32662) (0.27784)
4 Cointegrating Equation(s): Log likelihood 191.9129
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000 0.000000 0.000000 0.000000 0.418554 4.510621 -5.413011
(0.07920) (1.62130) (1.88837) 0.000000 1.000000 0.000000 0.000000 1.269167 13.81473 -18.01191
(0.22356) (4.57667) (5.33055) 0.000000 0.000000 1.000000 0.000000 1.419045 15.22291 -18.82875
(0.25809) (5.28371) (6.15406) 0.000000 0.000000 0.000000 1.000000 0.507871 5.171131 -5.970902
(0.08767) (1.79470) (2.09033)
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.551124 0.598774 0.264635 -1.019562
(0.30053) (0.43333) (0.36678) (0.18719)D(CLNGIR) -0.129850 0.790143 -0.596441 -0.174325
(0.15587) (0.22475) (0.19024) (0.09709)D(CLNEXRATE) -0.025100 0.665174 -0.566981 -0.083878
(0.08227) (0.11863) (0.10041) (0.05124)D(CLNDR) 0.246859 0.692367 -0.642271 -0.100603
(0.22377) (0.32266) (0.27311) (0.13938)D(CDEFICIT) 0.657485 14.96905 -11.42924 -6.230626
(3.70495) (5.34221) (4.52178) (2.30769)D(CLNM1) -0.049824 -0.272555 0.271485 -0.039643
(0.07457) (0.10752) (0.09101) (0.04645)D(CLNMB) -0.006513 -0.094519 0.073985 0.068226
(0.09006) (0.12986) (0.10992) (0.05610)D(CLSAINFL) -0.196416 1.256825 -0.657352 -1.034811
(0.19673) (0.28367) (0.24010) (0.12254)
5 Cointegrating Equation(s): Log likelihood 199.9226
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000 0.000000 0.000000 0.000000 0.000000 -1.181135 1.513872
(0.35167) (0.40285) 0.000000 1.000000 0.000000 0.000000 0.000000 -3.444200 2.992256
(1.21653) (1.39359) 0.000000 0.000000 1.000000 0.000000 0.000000 -4.074145 4.655830
52
(1.31526) (1.50668) 0.000000 0.000000 0.000000 1.000000 0.000000 -1.735215 2.434141
(0.73974) (0.84741) 0.000000 0.000000 0.000000 0.000000 1.000000 13.59862 -16.54956
(3.04075) (3.48331)
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.586370 0.420690 0.285853 -0.846529 -0.031338
(0.30246) (0.50915) (0.36462) (0.32450) (0.03690)D(CLNGIR) -0.161303 0.631220 -0.577507 -0.019910 0.013762
(0.15375) (0.25882) (0.18535) (0.16496) (0.01876)D(CLNEXRATE) -0.014408 0.719197 -0.573418 -0.136368 -0.013527
(0.08261) (0.13906) (0.09959) (0.08863) (0.01008)D(CLNDR) 0.325077 1.087581 -0.689357 -0.484607 0.019055
(0.20661) (0.34780) (0.24907) (0.22166) (0.02521)D(CDEFICIT) -0.011791 11.58739 -11.02635 -2.944883 -0.105307
(3.67698) (6.18972) (4.43267) (3.94494) (0.44865)D(CLNM1) -0.056216 -0.304851 0.275333 -0.008263 -0.001615
(0.07540) (0.12692) (0.09090) (0.08089) (0.00920)D(CLNMB) -0.011307 -0.118743 0.076871 0.091763 0.016982
(0.09136) (0.15380) (0.11014) (0.09802) (0.01115)D(CLSAINFL) -0.191616 1.281082 -0.660242 -1.058380 0.054517
(0.19989) (0.33648) (0.24097) (0.21445) (0.02439)
6 Cointegrating Equation(s): Log likelihood 207.5401
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.089862
(0.04088) 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 -1.160169
(0.06947) 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 -0.256075
(0.07225) 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.342117
(0.05751) 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 -0.154677
(0.52296) 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 -1.205628
(0.03570)
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.500686 -0.236569 0.238481 -0.963179 -0.040479 -1.096231
(0.27507) (0.54616) (0.32900) (0.29690) (0.03349) (0.83257)D(CLNGIR) -0.110449 0.241132 -0.605622 -0.089143 0.008337 -0.239403
(0.13378) (0.26561) (0.16000) (0.14439) (0.01628) (0.40490)D(CLNEXRATE) -0.016655 0.736430 -0.572176 -0.133309 -0.013287 -0.034484
(0.08337) (0.16553) (0.09971) (0.08998) (0.01015) (0.25233)D(CLNDR) 0.386737 0.614604 -0.723447 -0.568551 0.012477 -1.228106
(0.18548) (0.36827) (0.22184) (0.20019) (0.02258) (0.56139)D(CDEFICIT) 0.372549 8.639206 -11.23884 -3.468126 -0.146308 2.429518
(3.66577) (7.27847) (4.38448) (3.95659) (0.44624) (11.0953)D(CLNM1) -0.056875 -0.299798 0.275697 -0.007366 -0.001545 -0.002250
(0.07615) (0.15120) (0.09108) (0.08219) (0.00927) (0.23049)D(CLNMB) -0.026743 -0.000335 0.085405 0.112778 0.018628 0.432909
(0.08912) (0.17695) (0.10659) (0.09619) (0.01085) (0.26974)D(CLSAINFL) -0.149249 0.956096 -0.683666 -1.116058 0.049998 0.390001
(0.19090) (0.37904) (0.22833) (0.20605) (0.02324) (0.57781)
53
7 Cointegrating Equation(s): Log likelihood 212.4555
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CLNGIR CLNEXRATE CLNDR CDEFICIT CLNM1 CLNMB 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000
0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000
0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000
0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000
0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000
0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.526721 -0.160543 0.043738 -0.922470 -0.040981 -1.316857
(0.26887) (0.53635) (0.36783) (0.29149) (0.03260) (0.83601)D(CLNGIR) -0.113324 0.249527 -0.627128 -0.084647 0.008281 -0.263767
(0.13414) (0.26759) (0.18351) (0.14542) (0.01626) (0.41708)D(CLNEXRATE) -0.024736 0.760026 -0.632620 -0.120674 -0.013443 -0.102962
(0.08138) (0.16234) (0.11133) (0.08822) (0.00987) (0.25303)D(CLNDR) 0.395379 0.589368 -0.658802 -0.582064 0.012644 -1.154870
(0.18505) (0.36914) (0.25315) (0.20061) (0.02244) (0.57537)D(CDEFICIT) 1.056004 6.643439 -6.126579 -4.536776 -0.133137 8.221227
(3.28594) (6.55493) (4.49531) (3.56235) (0.39842) (10.2170)D(CLNM1) -0.051538 -0.315382 0.315619 -0.015711 -0.001442 0.042977
(0.07536) (0.15032) (0.10309) (0.08169) (0.00914) (0.23430)D(CLNMB) -0.009939 -0.049407 0.211106 0.086502 0.018952 0.575316
(0.07965) (0.15889) (0.10897) (0.08635) (0.00966) (0.24767)D(CLSAINFL) -0.162294 0.994190 -0.781246 -1.095660 0.049746 0.279452
(0.18905) (0.37712) (0.25862) (0.20495) (0.02292) (0.58781)
Table A112 COINTEGRATING RESULTS OF DISCOUNT RATE DIFFERENTIAL LONG RUN EQUATIONDate: 12/04/15 Time: 14:49Sample (adjusted): 1984 2013Included observations: 30 after adjustmentsTrend assumption: Linear deterministic trendSeries: CSASDDIFF CDEFICIT CGDPGR CLNMBLags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.806249 92.38297 47.85613 0.0000At most 1 * 0.494547 43.14747 29.79707 0.0008At most 2 * 0.379475 22.67847 15.49471 0.0035At most 3 * 0.243278 8.362798 3.841466 0.0038
Trace test indicates 4 cointegrating eqn(s) at the 0.05 level
54
* denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.806249 49.23550 27.58434 0.0000At most 1 0.494547 20.46900 21.13162 0.0617
At most 2 * 0.379475 14.31567 14.26460 0.0491At most 3 * 0.243278 8.362798 3.841466 0.0038
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
CSASDDIFF CDEFICIT CGDPGR CLNMB 0.954268 0.118537 -0.401090 0.411698 0.321945 -0.041128 0.601569 1.075057-0.718264 0.447443 -0.096965 -0.349599 0.230484 0.050277 -0.095753 -0.947634
Unrestricted Adjustment Coefficients (alpha):
D(CSASDDIFF) -0.836907 0.236621 0.092588 -0.201602D(CDEFICIT) -1.477499 0.427782 -2.325510 -0.214817D(CGDPGR) -1.070391 -1.196546 -0.356268 0.461444D(CLNMB) -0.031039 0.040181 0.002585 0.026093
1 Cointegrating Equation(s): Log likelihood -138.2925
Normalized cointegrating coefficients (standard error in parentheses)CSASDDIFF CDEFICIT CGDPGR CLNMB
1.000000 0.124218 -0.420312 0.431428 (0.05015) (0.08352) (0.16120)
Adjustment coefficients (standard error in parentheses)D(CSASDDIFF) -0.798633
(0.14647)D(CDEFICIT) -1.409930
(0.83561)D(CGDPGR) -1.021440
(0.44658)D(CLNMB) -0.029619
(0.01695)
2 Cointegrating Equation(s): Log likelihood -128.0580
Normalized cointegrating coefficients (standard error in parentheses)CSASDDIFF CDEFICIT CGDPGR CLNMB
1.000000 0.000000 0.708084 1.864973 (0.22938) (0.48638)
0.000000 1.000000 -9.084013 -11.54058 (1.84517) (3.91261)
55
Adjustment coefficients (standard error in parentheses)D(CSASDDIFF) -0.722454 -0.108936
(0.14511) (0.01808)D(CDEFICIT) -1.272208 -0.192732
(0.87661) (0.10921)D(CGDPGR) -1.406662 -0.077670
(0.38668) (0.04817)D(CLNMB) -0.016683 -0.005332
(0.01544) (0.00192)
3 Cointegrating Equation(s): Log likelihood -120.9002
Normalized cointegrating coefficients (standard error in parentheses)CSASDDIFF CDEFICIT CGDPGR CLNMB
1.000000 0.000000 0.000000 0.891529 (0.14986)
0.000000 1.000000 0.000000 0.947734 (0.66433)
0.000000 0.000000 1.000000 1.374757 (0.35300)
Adjustment coefficients (standard error in parentheses)D(CSASDDIFF) -0.788957 -0.067508 0.469041
(0.17638) (0.06626) (0.10402)D(CDEFICIT) 0.398123 -1.233265 1.075443
(0.86345) (0.32437) (0.50920)D(CGDPGR) -1.150767 -0.237080 -0.255936
(0.46461) (0.17454) (0.27399)D(CLNMB) -0.018539 -0.004175 0.036370
(0.01895) (0.00712) (0.01117)
TABLE AII3.
WALD SHORT RUN CAUSALITY TEST FOR THE DISCOUNT RATE DIFFERENTIAL
Decision:There is no short run causality if the p values for the chi-square are greater than 0.05
C (1) =Deficit
Wald Test:Equation: EQ02CSASDDIFF
Test Statistic Value df Probability
t-statistic 1.886785 27 0.0700F-statistic 3.559958 (1, 27) 0.0700Chi-square 3.559958 1 0.0592
Null Hypothesis: C(1)=0Null Hypothesis Summary:
56
Normalized Restriction (= 0) Value Std. Err.
C(1) 0.136673 0.072437
Restrictions are linear in coefficients.
P values for the Chi-square is 0.0592 and is greater than 0.05 therefore the deficit has no short run impact on the discount rate differential between South Africa and Swaziland.
C(2)=GDP growth
Wald Test:Equation: EQ02CSASDDIFF
Test Statistic Value df Probability
t-statistic 1.913820 27 0.0663F-statistic 3.662707 (1, 27) 0.0663Chi-square 3.662707 1 0.0556
Null Hypothesis: C(2)=0Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(2) 0.181627 0.094903
Restrictions are linear in coefficients.
P value for the Chi-square is 0.0556 and is greater than 0.05 therefore the GDP growth has no short run impact on the discount rate differential between South Africa and Swaziland.
C (3) =Money Supply M1
Wald Test:Equation: EQ02CSASDDIFF
Test Statistic Value df Probability
t-statistic -0.042893 27 0.9661F-statistic 0.001840 (1, 27) 0.9661Chi-square 0.001840 1 0.9658
Null Hypothesis: C(3)=0Null Hypothesis Summary:
57
Normalized Restriction (= 0) Value Std. Err.
C(3) -0.133523 3.112903
Restrictions are linear in coefficients.
P value for the Chi-square is 0.9658 and is greater than 0.05 therefore the Money supply (M1) growth has no short run impact on the discount rate differential between South Africa and Swaziland.
C (4) = Monetary base
Wald Test:Equation: EQ02CSASDDIFF
Test Statistic Value Df Probability
t-statistic 0.242883 27 0.8099F-statistic 0.058992 (1, 27) 0.8099Chi-square 0.058992 1 0.8081
Null Hypothesis: C(4)=0Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(4) 0.861558 3.547222
Restrictions are linear in coefficients.
P value for the Chi-square is 0.8081 and is greater than 0.05 therefore the GDP growth has no short run impact on the discount rate differential between South Africa and Swaziland.
TABLEII 4 BLOCK/JOINT VAR WALD TEST FOR CAUSALITY
VEC Granger Causality/Block Exogeneity Wald TestsDate: 11/11/15 Time: 10:06Sample: 1981 2013Included observations: 30
Dependent variable: D(CSASDDIFF)
Excluded Chi-sq Df Prob.
D(CDEFICIT) 3.557674 2 0.1688
58
D(CGDPGR) 31.41483 2 0.0000D(CLNM1) 7.253863 2 0.0266D(CLNMB) 2.484321 2 0.2888
All 47.34844 8 0.0000
The deficit has no short run impact on discount rate differential between South Africa and Swaziland as the p value for the chi-square is greater than 0.05.GDP growth has a short run impact on the discount rate differential between South Africa and Swaziland as the p value for the chi-square is less than 0.05.Money Supply M1 has a short run impact on the discount rate differential between South Africa and Swaziland as the p value for the chi-square less than 0.05. This is basically driven by the perception taken by economic agents on an increase in the debt stock as government procures debt through the issuance of bonds. Or there fall in domestic credit to the private sector as reflected in M1 encourages the monetary authorities to lag effect a lower domestic discount rate to that of South Africa. Jointly Money Supply has a short run impact on the discount rate differential. The fall in credit to the private sector in
Monetary base Mb has no short run impact on the discount rate differential between South Africa and Swaziland as the p value for the chi-square is greater than 0.05.
TableAII 5 VAR heteroscedasticity test Results.VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)Date: 08/04/15 Time: 15:42Sample: 1981 2013Included observations: 31
Joint test:
Chi-sq df Prob.
813.2114 784 0.2280
Individual components:
Dependent R-squared F(28,2) Prob. Chi-sq(28) Prob.
res1*res1 0.977925 3.164347 0.2684 30.31569 0.3483res2*res2 0.907536 0.701070 0.7429 28.13361 0.4574res3*res3 0.988483 6.130474 0.1497 30.64297 0.3332res4*res4 0.989231 6.561376 0.1407 30.66616 0.3321res5*res5 0.878937 0.518583 0.8358 27.24705 0.5048res6*res6 0.888686 0.570259 0.8084 27.54928 0.4885res7*res7 0.880778 0.527692 0.8309 27.30411 0.5017res2*res1 0.902307 0.659728 0.7629 27.97153 0.4660res3*res1 0.968238 2.177433 0.3636 30.01537 0.3625res3*res2 0.837640 0.368510 0.9163 25.96683 0.5749res4*res1 0.863107 0.450354 0.8727 26.75631 0.5315res4*res2 0.871209 0.483181 0.8549 27.00748 0.5178res4*res3 0.987226 5.520483 0.1647 30.60402 0.3349res5*res1 0.889618 0.575672 0.8055 27.57814 0.4870
59
res5*res2 0.915381 0.772687 0.7100 28.37680 0.4446res5*res3 0.901887 0.656592 0.7644 27.95849 0.4666res5*res4 0.980570 3.604856 0.2402 30.39768 0.3445res6*res1 0.608178 0.110870 0.9991 18.85352 0.9026res6*res2 0.790846 0.270083 0.9626 24.51622 0.6540res6*res3 0.632370 0.122866 0.9984 19.60346 0.8785res6*res4 0.756393 0.221783 0.9799 23.44818 0.7103res6*res5 0.662030 0.139917 0.9969 20.52292 0.8447res7*res1 0.978063 3.184655 0.2669 30.31995 0.3481res7*res2 0.917108 0.790281 0.7022 28.43036 0.4418res7*res3 0.900476 0.646276 0.7695 27.91477 0.4690res7*res4 0.959606 1.696868 0.4386 29.74779 0.3754res7*res5 0.962084 1.812459 0.4179 29.82462 0.3717res7*res6 0.954171 1.487144 0.4815 29.57929 0.3836
TableA116. Short run Inflation Equation (VECM) Vector Error Correction Estimates Date: 09/15/15 Time: 15:01 Sample (adjusted): 1984 2013 Included observations: 30 after adjustments Standard errors in ( ) & t-statistics in [ ]
Cointegrating Eq: CointEq1
CLNINFL(-1) 1.000000
CLNDR(-1) 0.230971 (0.10508)[ 2.19812]
CDEFICIT(-1) 0.088419 (0.00802)[ 11.0223]
CLNM1(-1) -0.049166 (0.26756)[-0.18375]
CLNMB(-1) 0.214946 (0.30049)[ 0.71532]
CLSAINFL(-1) -0.294681 (0.09263)[-3.18116]
60
C -0.038302
Error Correction: D(CLNINFL) D(CLNDR) D(CDEFICIT) D(CLNM1) D(CLNMB) D(CLSAINFL)
CointEq1 -0.538024 0.179879 -9.980481 -0.069273 0.077601 0.328223 (0.41438) (0.24668) (3.34109) (0.08229) (0.10530) (0.38954)[-1.29838] [ 0.72920] [-2.98720] [-0.84178] [ 0.73699] [ 0.84259]
D(CLNINFL(-1)) -0.173622 0.061715 9.809106 0.051416 0.025613 -0.286290 (0.35655) (0.21225) (2.87478) (0.07081) (0.09060) (0.33518)[-0.48695] [ 0.29077] [ 3.41212] [ 0.72613] [ 0.28271] [-0.85415]
D(CLNINFL(-2)) -0.220828 0.121614 5.452632 -0.039468 -0.080998 -0.256020 (0.27739) (0.16513) (2.23652) (0.05509) (0.07048) (0.26076)[-0.79610] [ 0.73649] [ 2.43800] [-0.71645] [-1.14916] [-0.98183]
D(CLNDR(-1)) 0.874727 -0.324784 12.19041 0.072141 0.036139 0.005206 (0.58402) (0.34766) (4.70883) (0.11598) (0.14840) (0.54901)[ 1.49777] [-0.93419] [ 2.58884] [ 0.62199] [ 0.24352] [ 0.00948]
D(CLNDR(-2)) -0.577624 -0.302913 6.840904 0.031689 0.104917 -1.333585 (0.61860) (0.36825) (4.98766) (0.12285) (0.15719) (0.58152)[-0.93376] [-0.82258] [ 1.37157] [ 0.25795] [ 0.66746] [-2.29328]
D(CDEFICIT(-1)) 0.003212 0.011233 0.392399 0.001574 0.001445 -0.013562 (0.02082) (0.01239) (0.16784) (0.00413) (0.00529) (0.01957)[ 0.15429] [ 0.90649] [ 2.33800] [ 0.38074] [ 0.27315] [-0.69305]
D(CDEFICIT(-2)) 0.022547 0.010620 0.000108 0.008476 -0.003406 0.045264 (0.02218) (0.01320) (0.17884) (0.00440) (0.00564) (0.02085)[ 1.01651] [ 0.80427] [ 0.00061] [ 1.92414] [-0.60427] [ 2.17084]
D(CLNM1(-1)) 2.339843 0.358379 20.83563 -0.290251 0.392389 0.399152 (1.32388) (0.78810) (10.6742) (0.26292) (0.33640) (1.24452)[ 1.76742] [ 0.45474] [ 1.95197] [-1.10397] [ 1.16644] [ 0.32073]
D(CLNM1(-2)) -0.159589 -1.049327 -4.991672 0.319899 0.228455 -0.402355 (1.18834) (0.70741) (9.58133) (0.23600) (0.30196) (1.11710)[-0.13430] [-1.48334] [-0.52098] [ 1.35552] [ 0.75658] [-0.36018]
D(CLNMB(-1)) -0.301156 -0.210984 10.37846 -0.133790 -0.495143 -1.549630 (1.30894) (0.77921) (10.5537) (0.25995) (0.33260) (1.23048)[-0.23008] [-0.27077] [ 0.98339] [-0.51468] [-1.48869] [-1.25937]
D(CLNMB(-2)) 1.465060 0.925711 22.66034 -0.255734 -0.212321 -0.978215 (1.20104) (0.71497) (9.68377) (0.23852) (0.30519) (1.12905)[ 1.21983] [ 1.29475] [ 2.34003] [-1.07217] [-0.69571] [-0.86641]
D(CLSAINFL(-1)) 0.311144 -0.067816 -4.794115 0.016274 -0.080182 0.123189 (0.29386) (0.17494) (2.36936) (0.05836) (0.07467) (0.27625)[ 1.05881] [-0.38766] [-2.02338] [ 0.27885] [-1.07380] [ 0.44594]
D(CLSAINFL(-2)) 0.252790 -0.220189 -7.985972 0.063173 0.003583 -0.194175 (0.27354) (0.16284) (2.20553) (0.05432) (0.06951) (0.25715)[ 0.92413] [-1.35219] [-3.62088] [ 1.16288] [ 0.05155] [-0.75511]
C -0.441224 -0.063411 -5.516866 0.192828 0.121570 0.217292 (0.25431) (0.15139) (2.05045) (0.05050) (0.06462) (0.23907)[-1.73499] [-0.41886] [-2.69056] [ 3.81802] [ 1.88130] [ 0.90892]
R-squared 0.640286 0.481192 0.783363 0.550203 0.489505 0.597197
61
Adj. R-squared 0.348018 0.059661 0.607346 0.184742 0.074728 0.269920 Sum sq. resids 2.216069 0.785320 144.0644 0.087402 0.143086 1.958350 S.E. equation 0.372162 0.221546 3.000671 0.073909 0.094567 0.349853 F-statistic 2.190751 1.141535 4.450487 1.505506 1.180165 1.824744 Log likelihood -3.486218 12.07476 -66.10410 45.00843 37.61444 -1.631729 Akaike AIC 1.165748 0.128349 5.340274 -2.067229 -1.574296 1.042115 Schwarz SC 1.819640 0.782241 5.994166 -1.413337 -0.920404 1.696007 Mean dependent -0.043255 -0.033108 0.099667 0.142697 0.120907 -0.024758 S.D. dependent 0.460908 0.228466 4.788651 0.081856 0.098311 0.409449
Determinant resid covariance (dof adj.) 1.55E-08 Determinant resid covariance 3.56E-10 Log likelihood 70.92328 Akaike information criterion 1.271781 Schwarz criterion 5.475373
TableA117 FINAL VAR STABLE FOR INFLATION EQUATIONDate: 11/12/15 Time: 09:53Sample (adjusted): 1984 2013Included observations: 30 after adjustmentsTrend assumption: Linear deterministic trendSeries: CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMBLags interval (in first differences): 1 to 2
Unrestricted Cointegration Rank Test (Trace)
Hypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.917752 164.9246 95.75366 0.0000At most 1 * 0.719539 89.98402 69.81889 0.0006At most 2 * 0.557740 51.84440 47.85613 0.0201At most 3 0.423246 27.36865 29.79707 0.0929At most 4 0.291963 10.85849 15.49471 0.2203At most 5 0.016552 0.500726 3.841466 0.4792
Trace test indicates 3 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
62
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
None * 0.917752 74.94055 40.07757 0.0000At most 1 * 0.719539 38.13963 33.87687 0.0146At most 2 0.557740 24.47575 27.58434 0.1190At most 3 0.423246 16.51016 21.13162 0.1964At most 4 0.291963 10.35776 14.26460 0.1895At most 5 0.016552 0.500726 3.841466 0.4792
Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):
CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB-4.346114 -0.558211 1.716699 -1.643488 1.684488 -1.465952 1.014829 -0.127408 4.756591 4.880303 2.247738 0.591197-8.020122 -0.007420 4.887953 1.395219 -0.937377 0.174645-4.978747 0.349602 2.740440 -5.514188 7.166552 -4.936033-1.488034 0.067189 2.970612 2.602021 0.858552 1.649104-1.549991 0.054254 2.950373 -4.004635 -1.169134 1.220616
Unrestricted Adjustment Coefficients (alpha):
D(CLNINFL) 0.081126 -0.117760 0.163580 0.063451 -0.025419 0.005061D(CDEFICIT) 1.682415 0.042550 -0.165635 -0.055319 -1.194249 -0.019785D(CLSAINFL) -0.059105 -0.160845 0.019462 -0.012146 -0.057771 0.003799
D(CLNDR) -0.040038 -0.034961 0.008086 0.011355 -0.027537 0.018625D(CLNEXRATE) -0.048362 0.009476 0.065705 -0.033249 -0.006658 0.002276
D(CLNMB) 0.000754 0.030767 -0.017078 0.020011 -0.021451 -0.003586
1 Cointegrating Equation(s): Log likelihood 51.00518
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000 0.128439 -0.394996 0.378151 -0.387585 0.337302
(0.01163) (0.10605) (0.14752) (0.12518) (0.08652)
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -0.352585
(0.30944)D(CDEFICIT) -7.311967
(2.48272)D(CLSAINFL) 0.256877
(0.24198)D(CLNDR) 0.174009
(0.17459)D(CLNEXRATE) 0.210187
(0.11474)D(CLNMB) -0.003279
(0.07788)
2 Cointegrating Equation(s): Log likelihood 70.07500
63
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000 0.000000 2.174990 2.618807 0.928476 0.461327
(0.48182) (0.63693) (0.54498) (0.37888) 0.000000 1.000000 -20.00937 -17.44527 -10.24657 -0.965631
(3.75239) (4.96035) (4.24427) (2.95068)
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -0.472091 -0.030282
(0.28933) (0.03712)D(CDEFICIT) -7.268785 -0.944564
(2.54906) (0.32702)D(CLSAINFL) 0.093647 0.053486
(0.17187) (0.02205)D(CLNDR) 0.138530 0.026804
(0.17499) (0.02245)D(CLNEXRATE) 0.219804 0.025789
(0.11735) (0.01505)D(CLNMB) 0.027945 -0.004341
(0.07223) (0.00927)
3 Cointegrating Equation(s): Log likelihood 82.31287
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000 0.000000 0.000000 0.435408 0.297733 0.082142
(0.19514) (0.18872) (0.12550) 0.000000 1.000000 0.000000 2.641451 -4.443888 2.522769
(1.71311) (1.65679) (1.10172) 0.000000 0.000000 1.000000 1.003866 0.289999 0.174338
(0.20968) (0.20279) (0.13485)
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -1.784024 -0.031496 0.378707
(0.46169) (0.02880) (0.35378)D(CDEFICIT) -5.940375 -0.943335 2.280980
(5.22839) (0.32619) (4.00638)D(CLSAINFL) -0.062438 0.053342 -0.771410
(0.35062) (0.02187) (0.26867)D(CLNDR) 0.073683 0.026744 -0.195507
(0.35939) (0.02242) (0.27539)D(CLNEXRATE) -0.307155 0.025301 0.283210
(0.18844) (0.01176) (0.14440)D(CLNMB) 0.164915 -0.004214 0.064163
(0.14328) (0.00894) (0.10979)
4 Cointegrating Equation(s): Log likelihood 90.56795
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000 0.000000 0.000000 0.000000 0.881165 -0.282934
(0.27371) (0.17415) 0.000000 1.000000 0.000000 0.000000 -0.904432 0.307995
(1.28635) (0.81846) 0.000000 0.000000 1.000000 0.000000 1.635145 -0.667372
(0.35332) (0.22481) 0.000000 0.000000 0.000000 1.000000 -1.339966 0.838469
(0.32526) (0.20695)
64
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -2.099929 -0.009313 0.552590 -0.829683
(0.49845) (0.03203) (0.36032) (0.36627)D(CDEFICIT) -5.664953 -0.962675 2.129380 -2.483424
(5.94633) (0.38206) (4.29851) (4.36948)D(CLSAINFL) -0.001966 0.049095 -0.804696 -0.593704
(0.39762) (0.02555) (0.28744) (0.29218)D(CLNDR) 0.017147 0.030714 -0.164388 -0.156154
(0.40778) (0.02620) (0.29478) (0.29964)D(CLNEXRATE) -0.141618 0.013678 0.192094 0.400740
(0.19603) (0.01260) (0.14170) (0.14404)D(CLNMB) 0.065287 0.002781 0.119001 0.014742
(0.15441) (0.00992) (0.11162) (0.11346)
5 Cointegrating Equation(s): Log likelihood 95.74683
Normalized cointegrating coefficients (standard error in parentheses)CLNINFL CDEFICIT CLSAINFL CLNDR CLNEXRATE CLNMB 1.000000 0.000000 0.000000 0.000000 0.000000 -1.316215
(0.55261) 0.000000 1.000000 0.000000 0.000000 0.000000 1.368560
(0.60510) 0.000000 0.000000 1.000000 0.000000 0.000000 -2.584792
(1.03361) 0.000000 0.000000 0.000000 1.000000 0.000000 2.409754
(0.83971) 0.000000 0.000000 0.000000 0.000000 1.000000 1.172631
(0.63051)
Adjustment coefficients (standard error in parentheses)D(CLNINFL) -2.062105 -0.011021 0.477079 -0.895825 0.151527
(0.49900) (0.03190) (0.38377) (0.38332) (0.36910)D(CDEFICIT) -3.887871 -1.042915 -1.418269 -5.590883 1.663139
(5.11476) (0.32698) (3.93367) (3.92898) (3.78332)D(CLSAINFL) 0.084000 0.045214 -0.976313 -0.744026 -0.615987
(0.37163) (0.02376) (0.28582) (0.28548) (0.27489)D(CLNDR) 0.058123 0.028864 -0.246190 -0.227806 -0.095869
(0.40545) (0.02592) (0.31182) (0.31145) (0.29990)D(CLNEXRATE) -0.131710 0.013230 0.172315 0.383416 -0.365752
(0.19723) (0.01261) (0.15168) (0.15150) (0.14589)D(CLNMB) 0.097207 0.001340 0.055278 -0.041075 0.211426
(0.14535) (0.00929) (0.11179) (0.11165) (0.10751)
Tables A118Deficit Testing for short-run effects
Wald Test:System: Untitled
Test Statistic Value Df Probability
Chi-square 1.034494 2 0.5962
65
Null Hypothesis: C(4)=0, C(5)=0Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(4) 0.003212 0.020816C(5) 0.022547 0.022181
Restrictions are linear in coefficients.
Money Supply Tests for short run effectsWald Test:System: Untitled
Test Statistic Value Df Probability
Chi-square 3.191835 2 0.2027
Null Hypothesis: C(10)=0, C(11)=0Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(10) 2.339843 1.323877C(11) -0.159589 1.188336
Restrictions are linear in coefficients.
Base Money/Seigniorage tests.Wald Test:System: Untitled
Test Statistic Value Df Probability
Chi-square 2.594742 2 0.2732
Null Hypothesis: C(12)=0, C(13)=0Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(12) -0.301156 1.308941C(13) 1.465060 1.201041
Restrictions are linear in coefficients.
Discount Rate testsWald Test:System: Untitled
Test Statistic Value Df Probability
Chi-square 7.318879 2 0.0257
66
Null Hypothesis: C(4)=0, C(5)=0Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(4) 0.874727 0.584019C(5) -0.577624 0.618600
Restrictions are linear in coefficients.
South Africa Inflation testWald Test:System: Untitled
Test Statistic Value Df Probability
Chi-square 1.191491 2 0.5512
Null Hypothesis: C(68)=0, C(69)=0Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(68) -0.080182 0.074671C(69) 0.003583 0.069508
Restrictions are linear in coefficients.
Swaziland Inflation testsWald Test:System: Untitled
Test Statistic Value Df Probability
Chi-square 0.644310 2 0.7246
Null Hypothesis: C(2)=0, C(3)=0Null Hypothesis Summary:
Normalized Restriction (= 0) Value Std. Err.
C(2) -0.173622 0.356548C(3) -0.220828 0.277387
Restrictions are linear in coefficients.
Table A119. Exogeniety Tests.VAR Granger Causality/Block Exogeneity Wald TestsDate: 09/08/15 Time: 14:21Sample: 1981 2013
67
Included observations: 31
Dependent variable: CLNINFL
Excluded Chi-sq Df Prob.
CLNGIR 0.399401 2 0.8190CLNEXRATE 2.342018 2 0.3101
CLNDR 12.50601 2 0.0019CDEFICIT 1.610262 2 0.4470
CLNM1 3.128409 2 0.2093CLNMB 0.756612 2 0.6850
CLSAINFL 2.560628 2 0.2780
All 41.67178 14 0.0001
Dependent variable: CLNGIR
Excluded Chi-sq Df Prob.
CLNINFL 0.827352 2 0.6612CLNEXRATE 8.387982 2 0.0151
CLNDR 0.257251 2 0.8793CDEFICIT 7.351952 2 0.0253
CLNM1 0.379786 2 0.8270CLNMB 0.480863 2 0.7863
CLSAINFL 4.389646 2 0.1114
All 54.48748 14 0.0000
Dependent variable: CLNEXRATE
Excluded Chi-sq Df Prob.
CLNINFL 1.930395 2 0.3809CLNGIR 23.95439 2 0.0000CLNDR 1.220419 2 0.5432
CDEFICIT 6.576954 2 0.0373CLNM1 0.527702 2 0.7681CLNMB 2.174435 2 0.3372
CLSAINFL 8.963543 2 0.0113
All 51.26944 14 0.0000
Dependent variable: CLNDR
Excluded Chi-sq Df Prob.
CLNINFL 3.149171 2 0.2071CLNGIR 1.767480 2 0.4132
CLNEXRATE 4.520153 2 0.1043CDEFICIT 1.581335 2 0.4535
CLNM1 2.644291 2 0.2666CLNMB 0.746233 2 0.6886
CLSAINFL 3.390071 2 0.1836
All 22.40084 14 0.0707
68
Dependent variable: CDEFICIT
Excluded Chi-sq Df Prob.
CLNINFL 0.699114 2 0.7050CLNGIR 1.830496 2 0.4004
CLNEXRATE 1.572106 2 0.4556CLNDR 4.207896 2 0.1220CLNM1 2.513337 2 0.2846CLNMB 1.330049 2 0.5143
CLSAINFL 3.855447 2 0.1455
All 21.21776 14 0.0962
Dependent variable: CLNM1
Excluded Chi-sq Df Prob.
CLNINFL 0.684351 2 0.7102CLNGIR 2.944836 2 0.2294
CLNEXRATE 7.442760 2 0.0242CLNDR 0.566650 2 0.7533
CDEFICIT 0.563306 2 0.7545CLNMB 0.709003 2 0.7015
CLSAINFL 3.566191 2 0.1681
All 16.40683 14 0.2892
Dependent variable: CLNMB
Excluded Chi-sq Df Prob.
CLNINFL 2.774556 2 0.2498CLNGIR 1.120270 2 0.5711
CLNEXRATE 2.305852 2 0.3157CLNDR 0.881585 2 0.6435
CDEFICIT 2.786904 2 0.2482CLNM1 6.672373 2 0.0356
CLSAINFL 2.755511 2 0.2521
All 20.62453 14 0.1116
Dependent variable: CLSAINFL
Excluded Chi-sq Df Prob.
CLNINFL 0.483881 2 0.7851CLNGIR 4.632949 2 0.0986
CLNEXRATE 7.893497 2 0.0193CLNDR 26.31771 2 0.0000
CDEFICIT 5.546403 2 0.0625CLNM1 1.963671 2 0.3746CLNMB 2.279033 2 0.3200
All 86.85901 14 0.0000
69
Error Correction: D(CLNINFL) D(CLNDR) D(CDEFICIT) D(CLNEXRATE) D(CLNMB) D(CLSAINFL)
CointEq1 -0.352585 0.174009 -7.3119673 0.210187 -0.003279 0.256877 (0.30944) (0.17459) (2.48272) (0.11474) (0.07788) (0.24198)[-1.13941] [ 0.99670] [-2.94515] [ 1.83189] [-0.04210] [ 1.06156]
Error Correction:D(CSASDDIFF
) D(CDEFICIT) D(CGDPGR) D(CLNINFL) D(CLNMB)
CointEq1 -0.820928 -0.969297 -1.151226 0.019106 -0.027248
(0.14670) (0.85219) (0.46797) (0.09061) (0.01693)
[-5.59606] [-1.13742] [-2.46003] [ 0.21087] [-1.60992]
VEC Granger Causality/Block Exogeneity Wald TestsDate: 12/21/15 Time: 14:30Sample: 1981 2013Included observations: 30
Dependent variable: D(CSASDDIFF)
Excluded Chi-sq df Prob.
D(CDEFICIT) 5.679320 2 0.0584D(CGDPGR) 18.66710 2 0.0001
3 The adjustment is not in log form hence the error correction is actually 7.3 percent per period.
70
D(CLNINFL) 3.071373 2 0.2153D(CLNMB) 10.71137 2 0.0047
All 29.94782 8 0.0002
Dependent variable: D(CDEFICIT)
Excluded Chi-sq df Prob.
D(CSASDDIFF) 3.454565 2 0.1778D(CGDPGR) 1.960839 2 0.3752D(CLNINFL) 5.111677 2 0.0776D(CLNMB) 0.166280 2 0.9202
All 7.967012 8 0.4367
Dependent variable: D(CGDPGR)
Excluded Chi-sq df Prob.
D(CSASDDIFF) 1.670144 2 0.4338D(CDEFICIT) 1.560546 2 0.4583D(CLNINFL) 2.051138 2 0.3586D(CLNMB) 4.197033 2 0.1226
All 10.90148 8 0.2073
Dependent variable: D(CLNINFL)
Excluded Chi-sq df Prob.
D(CSASDDIFF) 1.320540 2 0.5167D(CDEFICIT) 0.025910 2 0.9871D(CGDPGR) 0.057768 2 0.9715D(CLNMB) 1.010148 2 0.6035
All 2.425579 8 0.9651
Dependent variable: D(CLNMB)
Excluded Chi-sq df Prob.
D(CSASDDIFF) 4.400633 2 0.1108D(CDEFICIT) 0.492037 2 0.7819D(CGDPGR) 2.097247 2 0.3504D(CLNINFL) 4.177608 2 0.1238
All 10.19312 8 0.2517
71
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