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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