AIAE Macroeconomic Forecast SeriesWorking Paper 1
Designing and Operationalising
Macroeconomic Forecast
Model for Nigeria:
Context and Prospects
Published by African Institute for Applied Economics
First Published August, 2009
© African Institute for Applied Economics
All rights reserved. No part of this publication may be reproduced or transmitted in
any form or by any means, electronic or mechanical, including photocopying,
recording or any information storage and retrieval system, without permission in
writing from the copyright owner.
Table of Contents
List of Acronyms......................................................................................................6
AIAE Macroeconomic Forecast Working Paper Series ............................................7
Summary ................................................................................................................8
1.0 Introduction................................................................................................9
2.0 Bases and Objectives of the Modeling Initiative ........................................10
3.0 Review of Literature on Macroeconomic Modeling ...................................15
3.1 The Keynesian Model ..............................................................................15
3.2 The Vector Autoregressive Model (VAR) ..................................................17
3.3 Other Issues in the Literature ...................................................................18
3.4 Brief Review of Macroeconomic Models in Nigeria ...................................22
4.0 Methodology............................................................................................25
4.1 Conceptual Framework............................................................................25
4.2 Model Building and Identification..............................................................26
4.2.1 Model Specification....................................................................26
4.2.2 Model Estimation .......................................................................27
4.2.3 Model Diagnostic Checks...........................................................27
4.2.4 Model Forecasts ........................................................................29
4.2.5 Data Standards ..........................................................................29
4.2.6 Data Requirements, Sources and Availability .............................30
5.0 Expected Outputs and Deliverables .........................................................30
References ...........................................................................................................35
LIST OF ACRONYMS
AIAE African Institute for Applied Economics
BMI Business Monitors International
BPE Bureau of Public Enterprises
BVAR Bayesian Vector Autoregressive
CBN Central Bank of Nigeria
CEAR Centre for Econometric and Allied Research
CPI Consumer Price Index
DFM Dynamic Factor Model
DGP Data Generating Process
DMO Debt Management Office
FSDH First Security Discount House
GDP Gross Domestic Product
IFS International Financial Statistics
IT Inflation Targeting
MDG Millennium Development Goal
MPR Monetary Policy Rule
NBS National Bureau of Statistics
NEEDS National Economic Empowerment and Development Strategy
NKAP New Keynesian - Augmented Philips Curve
NKPC New-Keynesian Philips Curve
NNPC Nigerian National Petroleum Corporation
NPC New Philips Curve
PC Phillips Curve
RMSEs Root Mean Squared Errors
UNCTAD United Nations Conference on Trade and Development
VAR Vector Autoregressive Models
VECM Vector Autoregressive Error Correction
AIAE Macroeconomic Forecast Working Papers constitute one line of outputs of the
Institute's macroeconomic forecast initiative - a flagship programme of the Institute.
The Papers in the series contain reviews, analyses and discussions relating to the
theory, practice and challenges of developing and sustaining macroeconomic
forecasting models. The Series is designed to rapidly transmit less technical and
more generalist information for the purpose of informing, enlightening and
stimulating the scientific and policy-relevant discourse about macroeconomic
forecasting issues. The Series is intended for cross-disciplinary readership
audience in academia, government, civil society and development community.
AIAE Macroeconomic Forecasting Working Paper Series
This paper gives the background context and niche for the macroeconomic
forecasting initiative of the African Institute for Applied Economics. It explores the
experiential situation and literature landscape for macroeconomic forecasting in
Nigeria and the critical lessons and implication for the success and sustainability of
the present initiative.
Section 1 is the introduction. It gives the niche significance and institutional
challenges of macroeconomic forecasting in relation to economic and investment
decision-making in public and private sectors. Section 2 is a discussion about the
rationale, guiding principles and objectives of the AIAE macroeconomic forecasting
initiative. Section 3 is a review of the scientific literature on macroeconomic
forecasting models. It broadly identifies extant theoretical frameworks and empirical
approaches for designing and building macroeconomic forecasting models. Also, it
contains some overview of some attempts at macroeconomic forecast modeling in
Nigeria. Section 4 is a preview of broad principles, steps, conceptual approaches
and data requirements of the AIAE macroeconomic forecasting project. Section 5 is
an outline of the main outputs and other deliverables that will serve as key tools and
mechanisms for the regular validation, dissemination, utilization and review of the
results (information and knowledge) from the forecasting initiative.
Summary
1.0 INTRODUCTION
In every economy, decision-makers in public and private sectors require credible
and timely futures information as the basis for sound strategic policy and
management decisions. The prospects of economic agents such as firms are
closely tied to those of the broader economy (Bolliger, 2003; Guay, Haushalter and
Minton, 2003).
Financial analysts often incorporate macroeconomic shocks or risk variables that
affect individual companies into their earnings forecasts. This is done based on
forecasts generated by macroeconomic models and through incorporating
developments in the economy into the analyses of firms' current and future earnings
and investment choices. Likewise, households take crucial savings and
consumption decisions on the basis of projected trends in the broader economy. For
government economic planning and policymaking agencies, it is crucial to establish
sound evidence basis for decision-making.
Despite the relevance of macroeconomic forecasts to policy and management
across different segments of the economy, there are relatively weak scientific efforts
at producing needed forecasting frameworks. As a result, decision-making in the
country – at nearly all levels – has relied upon macroeconomic forecasts that are not
anchored on scientific models that track major economic indices. They rely rather on
observed outcomes of macroeconomic indicators, with potentials for significant
deviations and lags in decision-making.
In order to ameliorate the knowledge gaps, some organizations (both private and
public) have attempted to create own supply of models. Institutions like Central Bank
of Nigeria, Zenith Bank, First Security Discount House (FSDH) and Business 1
Monitors International (BMI) regularly work to at least discuss broad trends in the
macro economy and generally provide 'informed guesses' of its direction. But, many
of these efforts are weak principally because a number of technical and institutional
1 The Central Bank of Nigeria publishes the Economic and Financial Review; Zenith Bank publishes the Zenith Bank Intelligence
Quarterly; First Security Discount House Limited publishes the Nigerian Economy and Financial Markets: Review and Outlook while Business Monitors International (BMI) publishes the Macroeconomic Forecast for Nigeria.
limitations affect their relevance, use and applications.
Generally, the supply of macroeconomic models in Nigeria is faced with a number of
challenges. With mostly limited funding, (and often none for such relatively low
profit-yielding activities as model-building), the attraction to build models by
research institutions is low. Public sector funding for primary research is very limited
and most research institutions in Nigeria depend on donor funds.
There is also the challenge of duplicity, coordination and lack of cooperation
between economic agencies that have central stakes in macroeconomic modeling
and forecasting. Currently, there exist several splinters of models by different
institutions - both within academic and policy circles. But, such models are hardly
maintained. Unlike most other aspects of research that simply takes one snapshot of
the economy, models are dynamic; as the economy changes, models should ideally
be adjusted to reflect such changes. In this way, models are often considered living
organisms, and should maintain the same level of dynamism as the economy they
represent. Another key challenge is that of institutionalization of macroeconomic
models. Existing models in Nigeria are hardly 'institutionalized' beyond the
individuals that initiated them. This is because most existing models are mere
responses to one-off requests by willing clients.
Notwithstanding these challenges, the niche for macroeconomic forecasting as
critical evidence base for sound economic decision-making remains widely
acknowledged by both economists and practitioners. The growing need for
macroeconomic forecasting in decision-making (policies and planning at macro-,
meso- and micro- levels) underlies the motivation for AIAE's macroeconomic
forecast modeling initiative, under its Macroeconomic Analysis, Modeling and
Forecasting thematic group.
2.0 BASES AND OBJECTIVES OF THE MODELING INITIATIVE
With a population well over 140 million and the largest market in Africa, Nigeria is not
only a market to watch for today, it also represents Africa's tomorrow. It has been
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·the private sector appropriately monitor government actions and rightly set its own
expectations and indices to reflect realities within the economy;
· Providing guide to making effective and informed short- and medium term
economic plans and decisions by all segments of the society;
· Providing independent evaluation of the effectiveness or otherwise of
government policy actions and budget parameters under alternative
assumptions;
· Evaluation of government policy frameworks and their likelihood of yielding
expected outcomes and critically analyzing the assumptions underpinning them
with relevance to the actual outcomes in the economy;
· Tracking movements in critical aggregates in the economy and translating their
impact in accessible language to the daily needs of both policymakers and the
organized private sector;
· Providing a rallying point for independent evaluation and discussion of trends in
the economy as well as collating and crystallizing inputs from all segments of the
economy for effective translation into policy actions; and
· Providing needed intellectual anchor not just for ex-post assessment, but also for
ex-ante inputs into the policymaking process through rigorous analysis of
alternative policy scenarios and assumptions.
The need is to fill these gaps and provide intellectual leadership in weaving the
rigours of theory to the realities of day-to-day business and policy needs using
available data. Experiences with economic modeling and lessons from other
institutions that have been engaged in modeling and forecasting show a tension
between models that are rich enough to relate closely to the data and those that are
tractable enough to be useful for analysis of alternative policy choices. One of the
ways to overcome this problem is to build a small scale macro-econometric model
that has the structure necessary to conduct sensible policy analysis and capable of
supporting economic projections consistent with the macroeconomic environment.
However, in order not to be too simplistic as to be theoretically useless, such model
has to be anchored on a larger model that effectively reflects the dynamics of the
economy in a more disaggregated manner which can serve as its benchmark. In
addition, it has to be comparable to similar (sometimes atheoretic and structurally
different) models that track the data generating process of the economy most firmly.
Within this context, AIAE's modeling initiative combines 'appropriate' frameworks for
modeling that involves critical thinking on the model structure with an outreach
programme that elicits and incorporates regular inputs from diverse end-user
institutions and agencies. These will be structured within an in-house programme
that effectively disseminates skills among upcoming scholars in a way that ensures
sustainability of the programme. The approach is to adopt rigorous theoretical
processes that incorporate recent developments in the model-building literature
with current developments in the economy and use these to analyze their present as
well as make projections about their future trends and impacts. Secondly, using
simulations, the model will make alternative assumptions about shocks and relate
their implications for the evolution of selected macroeconomic indices in the
economy in a way that informs the policymaker on available options to ensure
minimal negative impact of such shocks on the economy. More importantly, the
current model is designed to exist as a “going concern” to meet up with the
challenges of policy shifts rather than be associated with a particular regime. In this
regard the current work shall be regularly updated. A major value added is that the
current model is self regulatory since the output from the forecasts will be
disseminated and communicated to the end users on a regular basis. One important
medium for doing so is the quarterly publications of “Economic Outlook” – a key
product under this initiative. Output from the forecasts will serve as a major input to
the publication and will provide a basis for independent evaluation of alternative
policy regimes
In the light of the foregoing, the overall aim of the AIAE macroeconomic forecasting
initiative is to generate and supply regular forecasts of key Nigeria macroeconomic
indicators to decision-makers in government, private sector and civil society. The
economic forecasts constitute leading-edge knowledge products in line with the
mission of the Institute – to promote evidence-based policies and decision-making
through research and critical analysis.
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3.0 REVIEW OF LITERATURE ON MACROECONOMIC MODELING
Generally speaking, there are two broad based frameworks for designing any
macro-econometric model. These are: the tradition theory-based which featured
prominently the traditional Keynesian model and the atheoretical model credited to
Sims (1980) which does not rely on any known theory – vector autoregressive
models, (VAR). Though, there are various stages of developments in economic
model history. They are collapsible into these two frameworks and would be
addressed in relation to their relevance to the current study.
3.1 The Keynesian Model
The traditional large macroeconomic models are often referred to as Keynesian
models because of their tenets in the idea that prices fail to clear markets, at least in
the short run. This contradicts the outstanding property of the then classical
dominance which rests on self-regulation of the price mechanism – the Says law of
the relationship between demand and supply. That is, the assumption that supply
will always create demand. The great depression of the 1930s and the subsequent
birth of the Keynesian non-market clearing model threw to the dungeon the market
clearing dominance of the classical equilibrium theory. The Classical Model largely
assumes the existence of an equilibrium point where product, labour and factor
markets clear and in a way is anchored on the micro behaviour of agents in an
economy. Such an equilibrium point is assumed to involve the full employment of
factors of production – particularly labour and capital.
The Keynesian model of constrained demand was built under the framework that in
a market economy there is a gap between supply and demand; and that output is not
a constraint but, deficient demand. The problem of deficient demand results in
unemployment given room for a model that does not bother on the supply-side
economy. This line of thinking generally made the original Keynesian model to
ignore the supply side, an idea that was criticized by the neoclassical economists.
The inability of the original Keynesian model to link the demand side to the supply
side of the economy was however addressed by the neo-Keynesian models starting
with the Hicks (1937) IS-LM model, which tried to simultaneously solve the product
and money markets and showed income and interest rates as linking variables that
clears the two markets. Today the simple IS-LM model as extended by Mundell-
Fleming (1963) has metamorphosed into a large scale model that links the real and
nominal variables. The dominance of the large Keynesian models like the Classical
was unable to address what has come to be known in economic theory as stagflation
– the combined effect of unemployment and inflation contrary to the Keynesian
theory of the inverse relationship between inflation and unemployment.
Generally, two more models emanated from the two foregoing lapses in the
traditional Classical and Keynesian models. These are the neo-classical business
cycle model which tried to explain the causes of business cycle and the responses of
the output to some exogenous shocks. The next was the Structuralist model that
dealt with the structural rigidities and bottleneck in the developing economies. The
Structuralist approach was a critique to the traditional models that links economies
to a preconceived economic theory. Economies manifest different characteristics
that should form the basis for the structure of the model design. In addition different
sectors in the same economy display different traits; therefore, the use of general
economic theory for specific country models necessarily does not mimic the given
economy.
While the Prebisch-Singer hypothesis-motivated structural model emerged as a
plausible representation of the developing economies and the changing structure of
the developed economies it does point to one basic stance. The basic stance of
structural models tacitly suggests that each variable in a macroeconomic model be
specified in its structural form giving rise to what could be called an eclectic
macroeconomic model. In all, macroeconomic models continue to advance to the
level of incorporating structural behavior of economies into the general economic
theory. In the 1970s the question of not considering rational expectation of economic
agents as part of the structure of an economy to be captured in a macroeconomic
model attracted the attention of modelers. What has come to be known as Lucas
(1976) critique was an attempt to address the laxity in the existing models to reflect
microeconomic foundations and economic realities to a robust predictive accuracy.
The after effect of the Lucas critique was the incorporation of expectations into
macroeconomic models, beginning with the adaptive expectation and latter rational
expectation. The model simply seeks to make adjustments for long run economic
optimization of various economic units in a system. Today, the backward and
forward-looking models have become major contributions to modern
macroeconomic forecasts. The development of macroeconomic models to reflects
country-specific macroeconomic conditions influenced the emergence of other
group of macro-econometric models void of any known theory – the atheoretical
macroeconomic models.
3.2 The Vector Autoregressive Model (VAR)
The Vector Autoregressive, (VAR) model was predicated on the possibility of
exogenizing of all macroeconomic variables. Sims (1980) was a response to the
perceived non-robust estimates of large scale models and the problem of
endogenizing some variables which constrains their performances in a model.
There is the possibility that macroeconomic variables are structurally related and
could have a bi-directional relationship irrespective of their economic theoretical
underpinnings. It is therefore questionable to endogenize some variables while
others are exogenized in the same model. Because of these foreseeable linkages
the model adopts a more flexible approach and does not dwell much on the a priori
structure, but on their dynamic relationships – not necessarily for parameter
estimation. Since the introduction of VAR, it has evolved to incorporate changes in
the properties of different data such as: data stationarity and co-integration of data
(long run relationship). As a result of these developments there is a structural VAR
(traditional VAR) and Vector Autoregressive Error Correction, (VECM) models.
In spite of the efficiency of VAR in small scale models and its ability to replicate the
structural relationship among variables, it does have various estimation challenges.
The efficiency of VAR lies in its dynamic structural specifications which in itself could
be problematic. There is the problem of optimal lag length which poses serious
estimation challenges because different lag length generates different estimation
results. In addition, because it is not a parametric-driven model it does not reflect the
impact of one variable on the other but the proportion of impact attributed to each
variable in the model through the impulse response function.
Nevertheless, the Keynesian model has remained the benchmark of macromodels
in the world. It continually incorporates the dynamisms in world economies giving
modelers room to adapt to structural changes in country-specific models. Currently,
the New Keynesian – Augmented Philips Curve, (NKAPC) model have addressed
the monetary policy challenges in various economies, adopting inflation targeting
(IT). It links the nominal and the real side of the economy by introducing the output
gap equation and the Monetary Policy Rule of monetary authorities, (MPR).
Therefore, the IS-MPR replaces the traditional IS-LM frame critiqued for not linking
the demand to the supply side of the economy. The New Philips Curve equation
(NPC) has provided that link for the Keynesian framework.
The conventional approach however, is the use of a benchmark model while
comparing it with the VAR as methodological diagnostic check. This approach not
only compares specification errors but also validates the choice methodology. The
current study adopts this approach by using VAR as a diagnostic check on the
chosen forecast model of the NKAPC.
3.3 Other Issues in the Literature
The empirical literature on forecasting models is huge and it is classified into three
basic areas namely; structural macro models, dynamic stochastic general
equilibrium models, and indicator models typically univariate and low-order VAR. It
is therefore, almost impossible to accommodate all of them in a single review.
However, we review most of the recent literature in this area for example Argov et al.
(2007), Bernajee et al. (2005), de Silva (2008), Liu and Gupta (2007), Gupta and
Kabundi (2008), Stock and Watson (1999), Manzan and Zerom (2009), Reijer
(2006), Clark and McCracken (2006), Rubaszek and Skrzypczynski (2009), Branch
and Evans (2006) among others, and show how they contribute in shaping the
present study.
Liu and Gupta (2007), develops a small-scale Real Business Cycle Dynamic
Stochastic General Equilibrium (DSGE) model for the South African economy to
forecasts real Gross National Product (GNP), consumption, investment,
employment, and a measure of short-term interest rate (91 days Treasury Bill rate),
over the period of 1970Q1-2000Q4. The out-of-sample forecasts from the DSGE
model is then compared with the forecasts based on an unrestricted vector auto-
regression (VAR) and Bayesian VAR (BVAR) models for the period 2001Q1-
2005Q4. They find that a Bayesian VAR with relatively loose priors outperforms both
the classical VAR and the DSGE model. Rubaszek and Skrzypczynski (2009),
studies the forecasting performance of a small-scale DSGE model and finds that
DSGE model was comparable or even better to the trivariate VAR and BVAR
models.
Gupta and Kabundi (2008), uses two-types of large-scale models, namely; the
Dynamic Factor Model (DFM) and Bayesian Vector Autoregressive (BVAR) Models
based on alternative hyper-parameters specifying the prior, which accommodates
267 macroeconomic time series, to forecast key macroeconomic variables of a
small open economy. Using South Africa as a case study and per capita growth rate,
inflation rate and the short-term nominal interest rate as the variables of interest,
they estimate the two-types of models over the period 1980Q1 to 2006Q4, and
forecast one- to four-quarters ahead over the 24-quarters out-of-sample horizon of
2001Q1 to 2006Q4. The forecast performances of the two large-scale models are
compared with each other, and also with an unrestricted three-variable Vector
Autoregressive (VAR) and BVAR models, with identical hyper-parameter values as
the large-scale BVARs. Their results, based on the average Root Mean Squared
Errors (RMSEs), indicate that the large-scale models are better-suited for
forecasting the three macroeconomic variables of their choice, and amongst the two
types of large-scale models, they find that DFM holds the edge.
Bernajee et al, (2005) applies the simple time series model to forecast the key
macroeconomic indicators such as measures of output growth, inflation and interest
rates of ten new EU countries. Due to data limitations, the authors adopt dynamic
factor models as an alternative forecast model. The relative performance of these
two forecasting approaches is compared by using data for five new Member States.
They find that factor models work well in general, although with marked differences
across countries.
de Silva (2008) develops a paper with two-fold objectives- the first is to present a
small macroeconomic model in state space form, the second is to demonstrate that
it produces accurate forecasts. The first of these objectives is achieved by fitting two
forms of a structural state space macroeconomic model to Australian data. Both
forms model short and long run relationships. Forecasts from these models are
subsequently compared, using the Wilcoxon-test which is a nonparametric test with
the null corresponding to the case when the state space forecasts are greater than
or equal to the SVAR alternative, to a structural vector autoregressive specification.
This comparison fulfills the second objective demonstrating that the state space
formulation produces more accurate forecasts for a selection of macroeconomic
variables. Argov et al, (2007) presents a small New Keynesian monetary model of
Israel's economy to analyse and forecast inflation, output gap, interest rates and
exchange rate which are the major variables in the monetary transmission
mechanism. They find their model generally satisfactory in forecasting the evolution
of the variables.
Stock and Watson (1998) uses a large number of macroeconomic time series, a
large number of nonlinear models, the investigation of unit roots pretest methods,
and an extensive investigation of forecast pooling procedures to implement forecast
comparison in which 49 univariate forecasting methods, plus various forecast
pooling procedures, are used to forecast 215 U.S. monthly macroeconomic time
series at three forecasting horizons over the period 1959-1996. The forecast
methods are based on four classes of models: auto-regressions, exponential
smoothing, artificial neural networks, and smooth transition auto-regressions.
According to their finding, the best overall performance of a single method is
achieved by auto-regressions with unit roots pretest and they argue that this
performance can be improved when it is combined with the forecasts from other
methods.
Branch and Evans (2006), compares the performance of alternative recursive
forecasting models. A simple constant gain algorithm, used widely in the learning
literature, and find that both forecast well out of sample. Clark and McCracken
(2006), provides empirical evidence on the ability of several different methods to
improve the real-time forecast accuracy of small-scale macroeconomic VARs in the
presence of model instability. They consider 18 distinct trivariate VARs each
comprising one of three measures of output, one of three measures of inflation, and
one of two measures of short-term interest rates. They compare their results to
those from simple baseline univariate models as well as forecasts from the Survey of
Professional Forecasters and the Federal Reserve Board's Greenbook. Their
results indicate that some of the methods so consistently improve forecast accuracy
in terms of root mean square errors (RMSE). They also find that the best method
often varies with variable being forecasted.
The standard approach for forecasting inflation has been the Phillips curve (PC)
model that, in its expectation-augmented version, assumes a trade-off between
unexpected inflation and unemployment, or more generally, indicators of real
economic activity. Despite its long-time success, recent empirical evidence on the
effectiveness of PC models is far from unanimous (Manzan and Zerom, 2009; Stock
and Watson 1999; Atkenson and Ohanian 2001; Fisher et al. 2002; among others).
For example, using U.S. data Stock and Watson (1999) provide a detailed study on
the out-of-sample forecast accuracy of the PC by using an extensive set of
macroeconomic variables. Using the forecast evaluation period January 1970 -
September 1996, their conclusion is that PC models have better forecasting
performances (compared to univariate time series models) using the unemployment
rate as well as other leading indicators of economic activity (e.g., output gap and
capacity utilization). Fisher et al. (2002) conducted a systematic comparison of the
forecasting accuracy (one-year ahead) of the naive and PC models in different sub-
periods and found that the PC forecasts outperformed the naive forecasts only in the
first sub period for most of the inflation measures that they considered. Atkenson
and Ohanian (2001), provides opposite empirical evidence, albeit a different
forecast evaluation where they report that PC models are no better than the naive
model, which assumes that the expected inflation over the next 12 months is equal
to inflation over the previous 12 months. As a result, in evaluating its forecasting
performance, the PC model is often compared with two time series models: the
autoregressive (AR) model and the naive or random walk model. Although simple,
these two time series models are very competitive benchmarks (Manzan and
Zerom, 2009).
These studies show that forecasts from VAR model are used as benchmark on
which the forecasts from theory consistent structural models are judged. Also, from
the literature it is evident that there is no single forecasting model that is superior to
others under different environments but researchers have also preferred theory
consistent models and then evaluate the accuracy of their forecasts based on the
forecasts from univariate, naive and VAR models. However, many researchers
agree that the standard approach to forecasting inflation has been the Phillips curve
(PC) model.
3.4 Brief Review of Macroeconomic Models in Nigeria
A major problem that has continued to militate against the successful design of
operational forecast model in Nigeria is the fact that most available models were
designed for a particular policy regime. While, historically, the building of macro-
economic model in Nigeria pre-dates independence in 1960, these models were
hardly updated as they were intended for specific policy purposes and were
generally abandoned after they served those purposes/regimes. Generally, a
policy-motivated model lacks the framework for regular update and
operationalization. In addition, being designed for political legitimacy, they are very
vulnerable to data mining and fishing. Such models work from answers to model
designs. Consequently, they are often time-bound and the motivation for regular
updates and simulation for use of answering questions posed by future
developments is limited. On the whole, research institutions in Nigeria have not
been able to develop institution-based models focusing on alternative policy
regimes or independent evaluation of government policies on the wider economy.
For instance, Ojo’s model use the original Keynesian aggregate demand framework
specified as a dynamic model, ignoring the monetary sector. The model was
designed as an input to the National Development Plan of (1962-1968). The study
inter alia revealed a positive influence of changes in world economic activity on
Nigeria's growth potential. Having satisfied the purpose of developing it, the model
was neither updated nor used any further after the development plan. The model's
inability to capture the monetary sector coupled with the lack of update reduced its
overall usefulness for periods after its design and use. More so the model made use
of low frequency data, a property not required for short and medium term planning.
In addition, the model though dynamic, used only a very short time frame (1951-
1965) calling into question the consistency of the estimates.
A number of international institutions like UNCTAD and the World Bank have
attempted to build macroeconomic models for Nigeria. However, these also suffered
from a number of limitations, not the least methodological and policy regime
coverage. Many of them, like the Ojo model, excluded the monetary sector, a key
determinant of private sector-led economic development. In an effort to address the
shortcomings identified in the non-inclusion of the monetary sector and time horizon
of the data applied by previous studies, Uwajere’s work specified a macroeconomic
model using a Harrod-Domar (HD) capital-constrained production function and
included aggregate demand, supply and monetary blocks. The model was designed
as a contribution to the medium-term national development programme. Like
previous studies, it is also a regime-motivated model whose live span depended on
the policy regime it served.
2Other studies built on the inclusion of the monetary sector include: Ghosh and Kazi
(1978), d'Alcantara et al. (1982) and Fair (1984). In all, d'Alcantara (1982) provided a
better understanding of the link between the financial sector and the rest of the
economy. However, the performance of the model was affected by data limitations
2 For details on macroeconomic models in Nigeria, see CBN macro-econometric model of Nigeria, (work-in-progress).
as it used low frequency data.
The tradition of building macroeconomic models for policy regimes continued till the
late 2006 with the CEAR model developed in 2006. The model was designed for the
second phase of National Economic Empowerment and Development Strategy
(NEEDS II). The model follows the same tradition as earlier models, the exception
being its further disaggregation of the sectors to capture the diverse nature of the
Nigerian economy, with much emphasis on the split between oil and non-oil sectors.
The Central Bank of Nigeria recently initiated efforts to build a macroeconomic
model for Nigeria. In both methodology and approach, the Central Bank's project
deviates from the traditional structure of modeling in the past. It promises to be the
first macro-econometric model in Nigeria that incorporates current econometric
tools – considerations of time series properties of macroeconomic variables and
even though it is targeted at helping the institution frame its inflation targeting
regime, it has the capacity of dealing with other issues in the economy. Moreover, it
uses high frequency (quarterly) data spanning 1985 to 2007, giving room for more
robustness of estimates from the model. Nevertheless, the CBN model is for internal
monetary policy management use. It is not designed to directly feed into the
decision-making processes of the private sector investors and other economic
agents.
In part, the AIAE modeling project intends to correct some of the lapses of the past.
But importantly too, being an independent research institute, the institute intends to
employ its resources into providing independent assessment of the macroeconomy.
Thus, the model will be an independent policy-evaluating model void of any external
interference. Outputs from the model will be regularly disseminated via the AIAE
quarterly outlook. It is intended that the model will serve as a monitor of discrete
economic events and a means of gauging their potential impacts even before they
occur. In part too, the economy is better off with more models. Soludo (2002) argued
that rather than models being seen as competitors, policy makers will be better-off
maintaining different models. There is no model that is designed to answer all
questions. Different models answer questions about different sectors and time
horizons of policies and events. These complement one another.
4.0 METHODOLOGY
4.1 Conceptual framework
There are two criteria that will guide the modeling of Nigerian economy. The first
framework will look at the monocultural nature of the Nigerian economy
characterized by consistent fiscal gyrations. The economy is import dependent and
relies on oil as a balancing item in trade accounts. The importance of oil will be fully
integrated in the model, disaggregating between oil and non-oil sector.
There are emerging theories on the need to have country-specific models that
replicate country's economic characteristics rather than just theory. Structuralist
modeling approach recommends a theory-consisted country-specific model that will
mimic the structure of the intended economy instead of entirely looking at theory-
mimicked model. Characterizing the Nigerian economy can at best be described as
eclectic; and while following the New-Keynesian Philips Curve (NK-PC), variables
will be selected based on these macroeconomic characteristics of the Nigerian
economy.
There are two broad-based opposing methods in economic forecasts models
identified in literature: the traditional macroeconomic model, built on the New
Keynesian Philip Curve (NKPC), non-market clearing; and the Vector
Autoregressive Model (VAR), built on the atheoretical model approach of Sims
(1980). These approaches have their relative strength and weaknesses. The
original Keynesian model was criticized for lack of linkage between nominal and real
variables. This shortcoming was however, addressed with the incorporation of the
New Philips Curve (NPC). The Phillips Curve relation between wage or price growth
and unemployment rates provided that key linkage for Keynesian macroeconomic
models. The VAR model is not based on any known theory and because it is an
approximation of large scale model, it tends to ignore some vital information.
The current study will adopt the New Keynesian Augmented Philips Curve,
(NKAPC). The NKAPC is a structural monetary model of equations describing the
transmission mechanism of monetary policy and the real variables. That is, it links
the Monetary Policy Rule (MPR), representing the Central Bank of Nigeria's
monetary policy rule to the traditional IS framework. This approach is well suited for
our chosen forecasts. The principal variables, essential for describing and
understanding the transmission mechanism in a small and open economy, are
inflation, the output gap, the exchange rate and the interest rate. In addition, it is a
framework targeted to capture the current CBN inflation targeting, (IT) policy.
As part of diagnostic checks on the performance of the (NKAPC), VAR will be used
as alternative model. We will estimate and compare the relative performance of
each method. This as a matter of practice is a validity check on the chosen
methodology. It is conventional for macroeconomic modelers to present alternative
or opposing models to their chosen model as a form of methodological validation.
This arises because of the popular argument in literature that theory-based models
do not capture the macroeconomic characteristics of the intended economy, while
the atheoretical-based models do.
There are two phases in the design of the model framework for the study, namely:
the model building phase and the forecasting phase.
4.2 Model building and identification
4.2.1 Model specification
The model building phase focuses on theory-based model/or historical data within
which the chosen model is specified. The primary emphasis is on how to capture
important characteristics of the Nigerian economy and at the same time be theory
consistent. However, we cannot rule out a trade-off between the two, especially in an
eclectic economy like Nigeria. In line with practice, the focus of every credible model
is to capture the historical data and replicate the Data Generating Process, (DGP),
within which the forecast is based.
The eclectic nature of the Nigerian economy could impose strict restriction making it
impossible to rely on a particular theory or a single model. However, experience has
shown that combination of different models in economic forecasts series could
outperform those with single approach. According to Manzan and Zerom (2009) in
an ever evolving macroeconomic environment, a particular prediction model might
outperform alternative models in one period and not in others. Thus, averaging
different forecasts may provide superior performance over time. In fact, the literature
on conditional mean forecasting has documented that combining forecasts from
different models typically achieves better performance compared to the (best)
individual models, and this has been shown by Stock and Watson (1999) and Ang et
al. (2006). In addition, simple combination schemes such as averaging forecasts,
achieves better performance than more sophisticated schemes. Timmermann
(2006) also documented an extensive survey of the empirical evidence and the
motivation for combining forecasts. The focus of our model specification is to
capture the structure of the economy with the concept of our variables of interest.
4.2.2 Model estimation
Model estimation is delineated into two. The first is estimation of the time series
properties of the imputed data in the final model. It has become conventional for data
scrutiny before they enter into the final model. Understandably, most
macroeconomic data are shown to possess properties not consistent with
information required in predicting the future of macroeconomic indicators. Time
series data are collected at different point interval thus, having seasonal differences
and influence of time imbedded in them. Under such condition the time series data is
said to be non-stationary and not appropriate for empirical analysis until they are
made stationary. Apart from the non-stationarity problem of variables, it is also
possible to have a situation where two or more variables in the model are tied
together such that they have joint impact that cannot be separated in the long run.
More technically, the variables are said to be co-integrated in the long run. Where
these problems exist, the need to estimate error correction model arises. As such,
these data checks should precede model estimation. The second is the estimation
and simulation of the forecast model.
4.2.3 Model diagnostic checks
Model diagnostic checks are designed for a proper evaluation of the reliability and
efficiency of results obtained from the model estimates. The current study will use
model diagnostic checks to be able to validate the stability of the predictive power of
the applied model. This ensures that the models are robust enough to make a good
forecast. More specifically diagnostic checks will involve the following:
a) Economic analysis
Economic analysis is concerned with the assessment and review of the ability of the
model to track the economic characteristics of the indicators, following known
economic theory or a priori - the sign and magnitude of each indicator.
b) Statistical analysis
The primary objective of statistical analysis is the evaluation of the importance
(statistical relevance) of each of the variables in explaining the behaviour of the
forecast indicators within some levels of statistical assumptions. That is, the
evaluation of the ability and sustainability of various variables required in explaining
the behaviour of the selected macroeconomic indicators.
c) Econometric analysis
Several types of models are designed according to their relative efficiency and
ability to mimic the data generating process (DGP). And ascertaining the reliability
and stability of the chosen model is dependent on the quality of its output which is
determined by certain econometric assumptions and tests: structural breaks, mean
reverting of the variables, homoscedasticity (constant variance), direct and indirect
relationships of the variables (autocorrelation), normality and other classical
assumptions of efficient estimator.
d) Model validation
Every forecast has an element of variance between the baseline data and the
forecast. However, the credibility of the forecast outcome is measured by the
difference between the actual and the forecast. Technically, it is measured in terms
of the error margin of the forecast which is used in determining the efficiency of the
model to be used.
4.2.4 Model Forecasts
The next step after model estimation is the forecasts phase. There are 6 (six)
existing methods: naive method (rule of thumb); extrapolation; leading indicators;
surveys; time series models and econometric models. In all, a successful
forecasting requires that regularities be captured. These regularities are informative
about the future. Therefore the proposed method should capture these regularities
yet, excluding the non-regularities.
The study involves in-sample and out-of sample forecast. The in-sample forecasts is
an attempt to use the outcome of the model estimates to replicate the historic data.
In a more general sense, it replicates the DGP by tracking the historical data –
mimicking the economy's history. This is the most important stage in economic
model building because predicting the future solely depends on the ability to
reproduce the past – it is a sin qua non that validates the ability of the model to track
the future. The out-of sample forecasts is the whole essence of designing forecasts
models. It is merely, evidence-based statement about the future, relying on the
historic data. One of the major problems associated with forecasting in economics is
that economies evolve over time and are subject to intermittent, and sometimes
large, unanticipated shocks (Clements and Hendry,1999). However, a good
forecast is not necessarily evaluated on its ability to predict the economy one-on-
one but, on the variance between the actual and forecast outcome. Thus, it is
necessary to adequately handle some challenges in data behavior such as,
structural breaks that regularly occurs in an inconsistent macroeconomic policy
environment like Nigeria.
4.2.5 Data Standards
Different countries use different methodologies for data collection and analysis. It is
also true that different agencies are shouldered with the responsibility of data
collection. For instance in some countries the Central Banks (CBs) are responsible
for data while in some, national statistics bodies are established. In between, some
countries run a multiple data collection system where by some designated
macroeconomic indicators are collected by (CB) and others by statistics institutions.
Consequently, the use of different methodologies for data and macroeconomic
indicator assessment constitutes serious challenge for efficient and effective
tracking of indicators. To overcome these problems, the study will use conventional
methods to eliminate data related problems. These include trend analysis of each
variable, stationarity and co-integration testing of the variables to be used in the
model.
4.2.6 Data Requirements, Sources and Availability
The requirement for the project is high frequency data (quarterly data) in line with
short and medium term forecast. These include: actual Gross Domestic Product
(GDP), potential Gross Domestic Product, consolidated government expenditure,
interest rate (domestic and foreign), exchange rate, inflation rate, consumer price
index (CPI), money supply, consumption expenditure, private investment and
Monetary Policy Rate (MPR).
The project has the capacity to generate the minimum data requirements for the
study. There are three major resources for data input in the study: AIAE (including
surveys and interaction with economic managers and investors), Central Bank of
Nigeria, (CBN) and National Bureau of Statistics, (NBS). Over the years AIAE has
been involved in data generation, collation and storage. Her recent collaboration
with CBN in building a macro-econometric model of the Nigerian economy offers the
team an opportunity to use CBN resources to collect quarterly estimates of major
macroeconomic indicators in Nigeria. In addition, AIAE has a close relationship with
NBS and as such, will use that window for collection of supplementary data required
for the project. Data required for this project are strictly Nigeria generated so as to be
able to mimic the (DGP) or data history unto which the outcome of the simulation
result and forecast would be evaluated and validated. However, data on foreign
interest rate is collected from International Financial Statistics, (IFS).
5.0 EXPECTED OUTPUTS AND DELIVERABLES
The macroeconomic modeling initiative will meet not only the needs of the present. It
is intended to be sustainable in terms of continuously serving as reference for future
work in modeling of the Nigerian economy. Consequently, outputs from the project
will be a mix of analytical papers that assess and make projections of trends in the
macroeconomy and dialogue/consultative meetings that bring together relevant
stakeholders including the model builders, academia, scholars, technocrats and
users of the products. Some of the envisaged outputs from the project include:
a) Working Papers. In line with the mission of the Institute, research papers,
which reflect literature reviews, qualitative assessments and quantitative
analyses of trends in the economy shall be produced. Most of such research
papers shall be published as AIAE working papers. In many cases, they shall
depict efforts to timely transmit information emanating from the different
segments of the project to the general public even before final works have
been concluded. This ensures that some findings are communicated to those
that need them before they get overtaken by events while the larger work is
still being undertaken.
b) Journal Articles. As an academic institution, findings from different
segments of the project shall ultimately be sieved and compiled into
publishable formats in reputable journals. The preparation of such journal
articles shall take different approaches and be based on the different findings
of the project. But on the whole, the target audience of such journal articles
shall be professionals in the field of economic and related sciences.
c) Quarterly Economic Forecasts. This is the principal output of the
macroeconomic modeling project. The Quarterly Economic Forecast Journal
is expected to be a prime publication of the African Institute for Applied
Economics and is to be issued quarterly. It shall summarize the major
developments in the economy and have a section that shall outline the
forecasts for the next quarter and beyond based on estimates from the
model. It is expected that such quarterly publication shall be the major outlet
through which AIAE fulfils one of its key objectives of providing intellectual
support to the emerging Nigerian economy, driven by the private sector. It will
be a reference point for public policy as well as private decision-making and
shall serve as the principal means of disseminating the Institute's intellectual
concerns to the general public. As such, the publication shall aim to be as
comprehensive, but succinct as possible. Unlike the journal articles
therefore, it shall aim at the non-economics public. The language and
packaging shall appropriately reflect this focus.
d) Book Publications. In addition to the research papers, AIAE intends to
regularize model-building in Nigeria. This implies designing a systematic
process of communicating, not just peculiarities and challenges, but also
prospects and opportunities of modeling in Africa as well as assembling
experiences of different researchers both within and outside Nigeria on
modeling. This, in our view, can best be achieved through book project(s) that
target a larger audience of students, the academia, policymakers and the
private sector. Such book projects generally have larger reach within the local
communities than journal articles. As such, the Institute will undertake at least
one major book project that will be the outcome of the efforts under this
project.
e) Training and Capacity Building. Capacity building will be a major
component of the AIAE modeling project. There will be two major segments
of training delivered under the project. The first will consist of students with
the prospects and interests to pursue a career in modeling or are involved in
projects that involve analytical models. The second set will consist of
policymakers and private sector decision agents that intend to improve their
skills on interpreting and/or using set models to forecast important
aggregates. The delivery mode will usually be diverse; the aim however,
remains to multiply the skills and equip different segments of the society to
not only appreciate but also adopt the culture of evidence-based analysis and
decision making that modeling naturally involves.
f) Dialogue and Consultation. One of the major challenges facing models in
Nigeria is the lack of update mechanism. Such lack arises mainly because
the demand for the models ends with the first client that requests for it. As ed
such, one means that will be adopted to make AIAE model sustainable is to regularly
involve end users in the process. This will be achieved through
encompassing advocacy meetings. Such meetings shall regularly be
structured as national modeling workshops in which other modeling
institutions can be invited to present findings from their models for
brainstorming purposes. But in addition, targeted meetings with specific
policy institutions like the National Planning Commission, Central Bank of
Nigeria, the Nigerian National Petroleum Corporation, the Debt Management
Office and Bureau of Public Enterprises as well as selected private sector
organizations shall be instituted to more appropriately communicate
implications of model outputs and policy choices they present.
g) Networking. Under the AIAE modeling Project, networking shall not only be
seen as a means to an end, but also an end in itself. This is primarily because
one major reason why model-building efforts of many institutions in the past
failed is the lack of appreciation of what is going on elsewhere. Consequently,
many models in Nigeria lack synergies with either past efforts or other
ongoing ones. While diversity remains a desirable characteristic of models
within any nation, it is important not to duplicate efforts in one area. But more
so, it is important that outcomes from different efforts benefit from criticisms
and inputs from others and that a culture of healthy rivalry is developed
among model builders in the country to ensure continuous improvement in
the quality of the end product. This generally augurs well for intellectual and
economic growth. In addition, the world has shrunk in space owing to
technology and it is easier to compare works and experiences across long
distances in short periods. With global economies being interlinked, it
becomes important that a modeling programme in Nigeria should equally
benefit from experiences from other parts of the world. As such, collaboration
and networking under the AIAE modeling project shall not be limited to only
institutions in Nigeria but shall be extended to global institutions involved in
model building and use.
The intended overall impact of these outputs is a change, not just in the modeling
culture, but also in the awareness, appreciation and use of models (and by
extension other aspects of quantitative data inputs) into private and public decision
making. While the programme will generate forecasts, the process of delivering and
communicating on the products is equally important in the design of this project.
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