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Inflation Threshold and Demand for Money Function in West

Africa Monetary Zone (WAMZ) Countries: Implication for

Inflation Targeting

*Osang, Paul Abijia, Orji Alexander Chinedu & Nwokoye, Ebele Stella

Department of Economics, Nnamdi Azikiwe University, Awka, Nigeria

*Corresponding Author

DOI : https://doi.org/10.51583/IJLTEMAS.2025.14020028

Received: 25 February 2025; Accepted: 04 March 2025; Published: 22 March 2025

Abstract: Two macroeconomic policy options toward achieving a sustainable inflation threshold as well as a stable demand for

money function which are capable of fostering pragmatic effort of achieving other macroeconomic goals within the West Africa

Monetary Zone (WAMZ) countries, but not much has been achieved. This study examines inflation threshold and demand for

money function in the West Africa monetary zone countries, providing insight into attaining the threshold level of inflation that is

considered optimally reasonable for maintaining a stable demand for money function. The study employs a quantitative data

analysis technique. Data used for this study are annual time series data spanning from 1980-2023 covering the West Africa

monetary zone countries. The study employed the autoregressive distributed lag (ARDL) bound testing model to test for time

series properties of the relevant data and the vector autoregressive (VAR) model to estimate the threshold level of inflation.

Furthermore, error correction modeling (ECM) approach was explored to identify both the short-run and long-run impact of

inflation threshold among other major determinants of demand for money function in selected countries in the West African

monetary zone. The findings indicate that the expected specified demand for money function was stable and well behaved in the

West African Monetary Zones countries. The result further showed that monetary expansion can be both inflationary and

deflationary, depending on the country’s economic structure and policy environment.

Keywords: Demand for Money, Inflation Threshold, WAMZ.

I. Introduction

Inflation threshold as a concept has become a common practice around the world, notably in the African continent. Inflation

threshold is a framework for monetary policy, characterized by public announcement of official quantitative target(s) for the

inflation rate over one or more times periods. Inflation threshold framework is the identification of a framework of a forward -

looking and quantifiable inflation target upon which policy decisions are based. Since economic units often develop future

expectations which influence their economic rationality, inflation threshold offers a more realistic guide to the desired path of

economic development to central bankers. This derives from the major features of the inflation threshold framework, including

the announcement of the set goals to the public, thereby eliciting a reasonable measure of commitments from monetary

authorities. Central bankers are aware that public knowledge about the set target constitutes a bench mark by which the

effectiveness of the policy is judge.

The demand for money function on the other hand refers to the relationship between the quantity of money that people wish to

hold and various factors that influence their decision. According to Keynes, the theoretical relationship between demand for

money and inflation threshold stems out of the decision for holding financial assets in the form of cash and bonds which is linked

to the level of output rather than growth. Specific to the West African Monetary Zone (WAMZ), consideration given to adopting

the inflation threshold framework needs thorough assessment given the region’s macroeconomic quagmire and most importantly

the readiness of the Central Banks to achieve agreed threshold, which invariably is critical to ascertaining confidence in the

domestic economy. In this regard, transitioning from the current monetary aggregate regime (with price stability being the main

objective and supported by a suite of forecast models) to something new will require an accurate and robust assessment of the

wider implications of such a policy shift across the different sectors of the economy (Jackson, 2021).

Statement of the Problem

The implementation of inflation threshold in Africa especially within the West Africa Monetary Zone (WAMZ) countries has

resulted in some increase in the inflation rate and, in some cases, achieving the target threshold in the WAMZ countries had

coursed an increase in the inflation rate from 19.25% in 2009 to 22.49%, 23.37%, 23.92%, 24.21%, 25.12%, 25.72%, and 28.97%

in 2014, 2016, 2017, 2018, 2019, 2020, and 2021 respectively.(World Bank,2023). However, the issue of attaining a sustainable

inflation rate within the threshold level of 3% and 10% still remain a problem. Countries in WAMZ as import dependent

economies where exchange rate movement have some potential influence on the inflation rate, established direct exchange rate

pass through (ERPT) to domestic prices. Given the current monetary policy stance of the Central Banks, where commitment is

made to price stabilization using the policy rate while allowing the exchange rate to float, continued currency depreciation tend to

have negative impacts on domestic prices and inflation. The abysmal performance of the WAMZ countries currencies against the

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major trading currencies (US Dollar, Euro, and the British Pound) has generated debate among economist. In addition,

governments fiscal operations policies especially inflationary financing of large budgetary deficits by the Central Banks in the

selected countries posed serious challenges to monetary management. With exception of surplus of 2.92% recorded between 2001

and 2004, all other periods witnessed negative budget deficit. These fiscal deficits were occasioned by credits through ways and

means advances from the Central Banks. The consequences of deficit financing have reflected in rapid growth of liquidity in

these economies. For instance, both the growth of M1 and M2 became heightened between 2015 to date with 45.5% and 48.2%

respectively while budget deficit average -12.90% being the highest of all periods of negative percents.

Despite the above policy interventions, most developing economies particularly countries from the West Africa region have

been eluded by low and stable inflation targeting threshold for a very long time. In Nigeria, Ghana, Sierra Leone, Guinea, Senegal

and Ivory Coast, for instance, inflation has continued and still posing a challenging treat to the realization of other crucial

economic policy objectives given their oscillating behavior for over three decades. Further, it has been considered as a drag on the

countries progress in the attainment of primary convergence criteria set by the West Africa Monetary Zone (WAMZ) for inflation

threshold and stability of the demand for money. Thus, if inflation is a major obstacle in stabilizing the demand for money, then it

readily follows that policy makers should aim at a low rate of inflation. At this juncture, the pertinent issues are: if inflation

constitutes an unavoidable evil in the economy, how then can it be minimized? What level of inflation threshold is commensurate

with stable demand for money? Attempt at solving the above raised issues largely depends on each country’s initial conditions,

policy environment as well as a host of other intervening factors which vary from one economy to another.

Although, a number of studies have been conducted on inflation threshold and demand for money function in the African region,

for instance, Rangarajan, (2022) specifically examined the inflation threshold and demand for money in some selected Africa

countries, but the study did not indicates the effects of inflation below a threshold level. Fielding, (2019), looked at inflation

volatility and demand for money in Sub-Saharan Africa, but the study did not imply the effect of inflation on productivity, since

demand for goods and services are meet by the supply. Adi & Riti (2014) examined the demand for real balances in six countries

in the West Africa Monetary Zone (WAMZ), but the result of their study showed that real income and interest rate are not

significant in explaining the threshold level of inflation. Faboya & Ajilore, (2018) who examined the existence of threshold

effects in inflation and demand for money relationship using data for WAMZ for the period 1970 – 2010, but the study did not

conclude on non-linearity of the inflation growth and stability of income. This study seeks to address the lacuna in the previous

studies employing time series data spanning from 1980 to 2023 by examining the effect of inflation on economic growth, the

stability of demand for money function on the inflation threshold and also analyzing the significance of real income and interest

rate in explaining the threshold level of inflation.

While this study is similar to this latter strand of evidences, our study however charts a distinct path on a number of fronts. First,

none of the studies on inflation thresholds conducted for WAMZ countries tested for time series properties of the variables used

since most macroeconomic time series data are known to plagued by unit root problem. Thus, this paper expresses skepticism

about the validity of the empirical results of most of these earlier studies. This skepticism is based upon the fact that it is now an

agreed consensus that it is inappropriate to apply conventional econometric techniques to non stationary time series to estimate a

regression with this type of data at best ignores important information about the underlying (statistic and economic) process of

generating the data and at worst leads to spurious results. (Harris, 2018). Secondly this study analyzes the impact of inflation

threshold on the stability of demand for money function in selected countries in the West Africa Monetary Zone (WAMZ)

augmented with some growth determining variables like interest rate, exchange rate, foreign interest rate, government

expenditure, nominal exchange rate and broad money which earlier studies conducted for WAMZ took for granted. Furthermore,

this paper employed autoregressive distributed lag (ARDL) model and the vector auto regressive (VAR) model to test for Six

WAMZ counties by carrying out individual country’s specific test and also carried out structural break test for inflation

threshold and the behavior of demand for money function in the WAMZ countries.

The broad objective of this study is to estimate inflation threshold and demand for money function in the West African Monetary

Zone (WAMZ) countries. However, The following specific objectives would also be : achieved

1. To examine the determinant of demand for money function in the West African Monetary Zone (WAMZ) countries.

2. To ascertain the stability of demand for money function in the West African Monetary Zone (WAMZ) countries.

3. To examine the implication of demand for money function in determining the inflation threshold in the West African

Monetary Zone (WAMZ) countries.

Having identified issues bordering on the subject matter under investigation, the study seeks to provide answers to the following

basic research questions;

1. What are the determinants of demand for money function in the West African Monetary Zone (WAMZ) countries?

2. How stable is the demand for money function in the West African Monetary Zone (WAMZ) countries?

3. What implication does the demand for money function have on the determination of inflation threshold in the West African

Monetary Zone (WAMZ) countries?

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II. Review of Related Literature

Conceptual Framework

Inflation threshold is a central banking policy that revolves around adjusting monetary policy to achieve a specified annual rate

of inflation (CBN, 2014). This is known as the target rate which is normally set at around 2% to 10%. The principle of inflation

threshold is based on the belief that long-term economic growth and financial stability is best achieved by maintaining stable

prices which is achieved by controlling inflation.

Inflation threshold can be compared with other central bank operating targets, such price level targeting and nominal gross

domestic product (GDP) targeting. Inflation threshold is a central bank strategy of specifying an inflation threshold as a goal and

adjusting monetary policy to achieving that threshold (Robert, 2016).

The demand for money function on the other hand refers to the relationship between the quantity of money that people wish to

hold and various factors that influence their decision. According to Black (2012), demand for money refers to the amount of

money people wish to hold or the function determining this. In other words, it is referred to as the desire to hold cash. Omanukwe

(2013) defined demand for money as the desire of holding of financial assets in the form of money that is, cash or bank deposits

rather than investments. This definition implies that demand for money is not limited to cash holdings (as suggested by Black,

2003) but includes bank deposits in current and savings accounts that are not held for investment purposes.

Theoretical Review

Theories of Inflation Threshold

a. Keynesian Theory.

The Keynesian theory provides an explanation on a possible link between inflation and economic growth through aggregate

demand and supply framework. According to this model, in the short run, the aggregate supply curve is characterized by upward

sloping trend rather than vertical. But if the aggregate supply curve were to assume a vertical line, it then means that any changes

on the demand side will only result into price changes. However, if it is upward sloping, changes in aggregate demand affect both

prices and output, (Dornbusch, et al, 1996). This is made possible because many factors drive the inflation rate and the level of

output in the short-run. These include changes in: expectations; labour force; prices of other factors of production, fiscal and/or

monetary policy.

b. Neo-classical Theory.

The earliest neo-classical models was championed by Solow (1956) and Swan (1956). The variants of these models produce

different conclusions on the nature of relationship between inflation-growth nexus. One such variants was articulated by Mundell

(1963) who stated that an increase in inflation or inflation expectations immediately reduces people‘s wealth arising from a fall in

the rate of return on individual‘s real money balances. Greater savings means greater capital accumulation and thus faster output

growth. Tobin (1965) is another neoclassical economist, whose framework shows that a higher inflation rate permanently raises

output level but the effect on output growth is temporary.

Theories of Demand for Money

a. Fisher’s Quantity Theory

Fisher’s quantity theory was proposed by Fisher (1911). According to Schmitt (2003), Fisher’s quantity theory of money states

that there is a direct relationship between the quantity of money in an economy and the level of prices of goods and services. The

mathematical relationship between money stock and price is popularly called the equation of exchange in Fisher’s theory.

Fisher’s analysis on the transactions velocity of circulation of money, which refers to the rate at which money passes from one

hand to another, begins with a simple identity. There are always two parties to each transaction, represented by a seller and a

buyer. This implies that the value of receipts for the aggregate economy must equal the value of sales.

b. Cambridge Cash Balance Version

A different approach to the theory of demand for money was developed by Cambridge economists such as Pigou (1917), Marshall

(1920) and Keynes (1923). Keynes contribution to the cash balance version was through his 1923 tract on Monetary Reform (this

treatise was written before the 1936 general theory that gave birth to Keynesians liquidity preference). The Cambridge

economists advocated a quantity theory of money that paid more attention to money demand that the supply-oriented classical

version. The Cambridge economists argued that a certain portion of money supply will not be used for transactions; instead, it

will be held for the convenience and security of having cash in hand. In others words, the Cambridge cash balance emphasizes

that money acts both as a store of wealth and a medium of exchange.

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Empirical Literature

2.2.1. Empirical Literature Review on Inflation Threshold.

Christofferson (2016) investigated the nonlinear relationship between inflation and growth for 22 transitional countries over the

period from 1990 to 2010, using Sarrel’s (1990) approach to modeling the kinked interaction between inflation level and

economic growth. As a result, the study found that threshold level of inflation is 13%.The study did not find any evidence that

output will rapidly increased by high inflation for countries that keep inflation below this threshold level. The result showed that

policy makers should keep inflation at some specific threshold level where the favouable impact of inflation on growth

performance is the highest.

Fischer et al (2018) investigated the link between inflation and growth in time – series, cross section and panel data set for a large

numbers of African countries. The main result of these works is that there is a negative impact of inflation on growth. The study

utilized domestic exchange rate, per capita income, consumer price index and terms of trade as explanatory variables. The result

obtained shows that a negative relationship exists only when high inflation data were included in the sample. He further submitted

through his estimation that 10% threshold of inflation reduces real GDP per capita by 0.2% per year.

Sarrel (2019) explored the relationship between inflation threshold and demand for money in Southern African countries using

the Engel Gremger and JML technologies for the period 1980-2010. The study found the evidence of structural break in

interaction between inflation and growth. The result shows that the estimated threshold level equals to 8 percent, exceeding which

leads to negative, powerful and robust impact on inflation growth.

Kremer et al (2020) using time series data provides new evidence on the effect of inflation on long-run demand for money for a

panel 63 industrial and non industrial countries. The empirical evidence show that inflation impedes growth if it exceed threshold

of 2% for non-industrial and 12% for industrial countries respectively. The study however, indicates that below these thresholds,

the effect of inflation on growth are positively significant.

Farin (2022) investigated the relationship between inflation and output for the economy of Tunisia where permanent inflationary

shock has been observed for the last three decade 1990 to 2010. Using a bivariate vector auto-regressive composed of output

growth and the change in inflation in order to test the hypothesis that inflation has long run impact on output. He also use the data

for the same period to estimate the short run relationship between inflation and real output. The findings verify sidrauski’s

superneutrality of money which can be defined as inflation has no real effect on output in the short run.

Using the Ordinary Least Square (OLS) estimation method, Sriran (2013) investigated the demand for broad money function in

the Gambia, utilizing monthly data from 1988 to 2007, using an error correction model. The dependent variable is real money

balance, while the independent variables included real GDP, interest rates on deposits at the commercial banks, yields on the

Treasury bill and expected inflation. The cointegration analysis showed that there is a long-run relationship between real money

balance and its determinants. However, the result showed that variables such as foreign interest rates and expected depreciation

were not significant determinants of real money demand in Gambia.

Tanu (2014) using quarterly data for the period 1978 to 2010 estimated the demand for money for real broad money (M3) in New

Guinea with the aim of establishing its determinant and the functional stability. As most time series data non-stationary, which

potentially may lead to the spurious regression problem, individual data series were tested for unit root using the Augmented

Dickey Fulls (ADF) and Kwiatkowski-Philips-Schmidt-Shin (KPSS) tests. Showed that variables defined in levels are non-

stationary but are stationary in first differences i.e. integrated of order.

Adi and Riti (2014) examined the demand for real money balances in the West Africa Monetary Zone (WAMZ) from 1985 to

2014, using panel cointegration technique. The result of the estimation showed that real income and inflation rate positively

influenced broad money demand in the long-run. Further analysis showed that income elasticity was greater than unity in the long

term and less than unity in the short-run. The result further showed that both the currency substitution and capital mobility

hypothesis were confirmed for the long-run, but only the capital mobility hypothesis held in the short-run. The authors

recommended that monetary aggregate should grow more slowly than economic growth so as to maintain price stability. Also,

there is need for countries to maintain stable exchange rate and ensure a market driven interest rate policy.

Nehora and Adama (2022) examined the demand for broad money and its stability in Ghana. The study covered the period of

1990 to 2020, with annual time series M1, M2, GDP and interest rate, Johansen’s cointegration approach and error correction

mechanism were employed. The study estimated the results using two set of variables for real demand for money: M1 and M2.

This was done given the assumption that the demand for money was equal to the supply of money. The results show that, GDP

affects the level of demand for money in the long-run while the interest rate affects it in the short-run. The error correction term in

each of the cases shows that 18% of the deviations in the real demand for money are corrected annually. The CUSUM tests of

parameters stability showed that, the money demand functions was stable over the period and the chow test indicated that there

was no structural breaks.

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Identified Research Gap

Having sourced empirical literature on the subject matter, it was discovered that there was a paucity of empirical works on

inflation threshold and demand for money function across the globe. Thus, the few that were available were reviewed. However,

none of these empirical works covered the West Africa Monetary Zone (WAMZ) countries. The majority were done for the euro

zone, while some were carried out in some regions of Africa and Asia. The most recent empirical work on inflation threshold in

the West Africa Monetary Zone (WAMZ) countries did not examine the existence of threshold effect in inflation and demand for

money relationship, and did not conclude on non-linearity of the inflation growth and stability of money. This study seeks to

address the lacuna in the previous studies by employing time series data spinning from 1980 to 2023 and examined the effect of

inflation on growth, the stability for demand for money function on the inflation threshold and also analyzing the significance of

real income and interest rate in explaining the threshold level of inflation.

III. Research Methodology

Given the type of data used in this study-cross-sational and time series-panel data analysis was used since it made if possible to

address issues with autocorrelation and heteroskhedasticy that arise with pure time series analysis. Additionally, the techniques

employed are of the three variations of panel analysis, which comprises fixed, random effect, or pooled Ordinary Least Squares;

the selection which is based on diagnostic test such as the Huasman and Wald redundant tests.

Theoretical Framework

The theoretical framework for this study is anchored on the model quantity theory of demand for money. The monetarist demand

theory advanced by Friedman (1956) has been viewed as a restatement of the classical quantity theory. As noted by Jhingan

(2004), these classical economists did not explicitly formulate demand for money theory but they emphasized the transactions

demand for money in terms of the velocity of circulation of money. This according to the classical economists, is because money

acts as the medium of exchange and facilitates the exchange of goods and services. This views were expressed in the Fisher’s

equation of exchange.

MV= PQ

Where:

M= the quantity of money, V= its velocity of circulation, P= price level and Q= total output.

Here, MV= money supply, while PQ represents the demand for money.

At equilibrium, money demand (PQ) equals money supply (MV), Jhingan (2004) further noted that the underlying assumption in

the equation of exchange is that people hold money for transactionary purposes and does not explain other motives for holding

money. Friedman (1956) faulting the classical theory argued that money is held for purposes other than transactions purposes.

According to Friedman (1956), investors can hold their wealth in the form of money, bonds, equity shares and commodities. He

concludes that demand for money depends on rates of returns of the four assets and upon income.

Model Specification

Demand for Money Function Model

The model used in this study is an adopted form of the model specified in Friedman modern quantity theory (1956). The model is

adopted for this as it embodies a number of variables similar to the variables of interest in this study and indicate some

fundamental theoretical determinants of demand for real balances. Equation 3.1 is augmented to accommodate variables of

demand for real balances and some other control variables so as to capture their impact on demand for money and its stability.

The functional form of the model is given as;

M2 = F (RGDP, INFR, INTR, EXCH, FISDEF, GEXP) 3.1

Where

M2 = real broad money, RGDP = real gross domestic product, INFR = Inflation rate, INTR = interest rate, EXCH = Exchange

rate, FISDEF = Fiscal deficit, GEXP = Government expenditure, which typifies as a measure of fiscal dominance.

Mathematically, the model is specified as; M2 = β

0

+ β

1

RGDP + β

2

INFR + β

3

INTR + β

4

EXCH + β

5

FISDEF + β

6

GEXP

3.2

The model can be restated in a stochastic form as:

M2 = β

0

+ β

1

RGDP + β

2

INFR + β

3

INTR + β

4

EXCH + β

5

FISDEF + β

6

GEXP 3.3

Where β

0

is the constant term or parameter intersect and β

1

, β

2

, β

3

, β

4

, β

5

, β

6

are parameter estimates of real gross domestic

product, inflation, interest rate, exchange rate, foreign interest rate and government expenditure respectively

Taking the natural logarithm for the model we have.

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LnM2 = M2 = β

0

+ β

1

LNRGDP + β

2

LNINFR + β

3

LNINTR + β

4

LNEXCH + β

5

LNFISDEF

+ β

6

LNGEXP 3.4

Inflation Threshold Model

From the demand for money function model, the money demand variables are treated as significant determinants of the inflation

threshold model using the function of function rule. The model specification for this study is based on the popularized version of

the DOLS procedure by Sims (1986; 1980), which is expressed in Equation 3.5. According to Brooks (2008), a dynamic ordinary

least square is a systems regression model that comprises both univariate time series and simultaneous equations models in which

all variables are treated as endogenous.

INF GAP=F(MD) + EXCHR + GEXP + NEXCHR + M2 + YP(Y –Y ) + FISDEF ) 3.5

Where:

INF GAP= Inflation Gap (Difference between CPI and the targeted rate )(-)

MD = Money Demand Variables

EXCHR = Exchange Rate

GEXP = Government Expenditure

NEXCHR = Nominal Exchange Rate

M2 = Broad Money

YP ( Y- Y

t

) = Output Gap ( Difference between trend from the composite index of economic activity and actual CIEA)

FISDEF = Fiscal Deficit

Mathematically, the model is specified as

INF – GAP = F(MD) + EXCH + GEXP NEXCH + M2 + YP ( Y- Y

t

) + FISDEF 3.6

The model can be restated in a stochastic form as

INF – GAP = β

0 +

β

1EXCHR

+ β

2GEXP

+ β

3NEXCHR

+ β

3M2

+ β

5YP

+ β

6FISDEF + U

3.7

Where β

0

is the constant term or parameter

intercept and β

1,

β

2,

β

3,

β

4,

β

5,

β

6

are parameter estimates of exchange rate, government

expenditure, nominal exchange rate, broad money, output gap and fiscal deficit respectively.

Taking the natural logarithm for the model we have

LnINF = DD = β

0 +

β

1EXCHR

+ β

2GEXP

+ β

3NEXCHR

+ β

4M2

+ β

5YP

+ β

6FISDEF

+ U 3.8

Where Ln = Natural Logarithm

The Logarithm is taken in order to allow for easy interpretation of their coefficient as elasticity

Presentation and Interpretation of Results

This section presents a comprehensive econometric analysis and an in-depth discussion of the study's findings. It is organized into

two major premises. The first premise delves into the various factors that determine the demand for money, exploring how these

variables interact within the economic framework. The second premise shifts focus to the determinants of inflation threshold,

analyzing the key variables and conditions that influence the effectiveness of inflation- threshold policies among the selected

countries under study

IV. Result Presentation and Analysis

Test Results of Demand for Money Function Determinants

This section presents the results of the demand for money function model for six African countries: Nigeria, Ghana, Sierra Leone,

Guinea, Gambia, and Liberia. the section presents the results of the, Descriptive Statistics, Correlation Matrix, Augmented

Dickey-Fuller (ADF) test to examine the stationarity of the time series data. Following this, the section details the estimations

from the Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration, which identifies the presence and

extent of a long-run equilibrium relationship between the variables. The discussion includes both the long-run and short-run

dynamics derived from the model. Furthermore, the chapter evaluates the robustness and validity of the model through post-

estimation diagnostic tests, including tests for heteroskedasticity, multicollinearity, and normality. These tests ensure the model's

efficiency and reliability. Lastly, the stability of the econometric model is assessed using stability tests to verify the consistency of

the results over time.

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Descriptive statistics

Table 4.2 provides a detailed overview of the economic performance of six West African countries: Nigeria, Ghana, Sierra Leone,

Guinea, Gambia, and Liberia. It presents descriptive statistics for key economic indicators, including broad money (M2), which is

a proxy for demand for money, real GDP (RGDP), inflation rate (INFR), exchange rate (EXCH), fiscal deficit rate (FISD), and

government expenditure as a percentage of GDP (GEXP) over period from 1980 to 2023. This table offers a snapshot of each

country's economic landscape, highlighting both areas of strength and potential vulnerabilities.

Table 4.1: Descriptive Statistics

Variable

Obs

Mean

Std. dev.

Min

Max

Nigeria

M2

44

17.27

6.32

9.10

28.60

RGDP(‘bn)

44

278.97

152.26

114.54

550.65

INFR

44

18.87

16.15

5.39

72.80

EXCH

44

17.02

4.88

8.40

31.60

FISD

44

126.08

144.01

0.55

638.70

GEXP

44

-1.45

3.25

-6.10

8.76

Ghana

M2

44

23.60

6.57

11.30

34.10

RGDP

44

29.14

19.17

9.12

70.51

INFR

44

26.66

24.42

4.90

122.90

EXCH

44

16.47

7.19

8.89

35.76

FISD

44

1.65

2.46

0.00

11.02

GEXP

44

-4.65

3.21

-15.29

0.00

Sierra Leone

M2

44

14.15227

8.647691

0

29.4

RGDP

44

3.056364

1.264231

1.72

5.58

INFR

44

30.94795

35.80175

-0.92

178.7

EXCH

44

0.1927273

17.06889

-51.62

27.15

FISD

44

3.353409

4.312431

0

21.3

GEXP

44

3.663636

9.271856

-8.9

20.08

Guinea

M2

44

14.15

8.65

0.00

29.40

RGDP

44

3.06

1.26

1.72

5.58

INFR

44

30.95

35.80

-0.92

178.70

EXCH

44

0.19

17.07

-51.62

27.15

FISD

44

3.35

4.31

0.00

21.30

GEXP

44

3.66

9.27

-8.90

20.08

Gambia

M2

44

25.17

10.59

15.10

52.20

RGDP

44

12.56

5.83

5.92

26.23

INFR

44

3.84

6.33

-4.10

32.30

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EXCH

44

9.81

2.02

5.80

13.00

FISD

44

482.24

133.00

211.28

732.40

GEXP

44

-3.22

2.94

-8.70

5.90

Liberia

M2

44

24.69

7.08

13.10

38.60

RGDP

44

33.59

14.08

20.54

71.99

INFR

44

4.23

4.79

-1.10

26.10

EXCH

44

10.86

5.41

2.50

16.80

FISD

44

482.24

133.00

211.28

732.40

GEXP

44

-4.26

2.02

-8.38

0.56

Note: M2= Demand for Money, EXCH=Exchange Rate, FISD= Fiscal Deficit Rate, GEXP=Government Expenditure, INFR=

Inflation Rate, INTR=Interest Rate, RGDP=Real GDP

The statistics for Nigeria reveal a mixed economic performance. The average real GDP (RGDP) of $278.97 billion indicates a

relatively large and growing economy, though with significant variability (standard deviation of $152.26 billion), reflecting

periods of both rapid growth and slowdowns. The demand for money (M2) averages 17.27%, indicating moderate liquidity in the

economy. However, Nigeria faces substantial inflationary pressures, with an average inflation rate (INFR) of 18.87%, and this has

fluctuated widely, reaching as high as 72.80%. The exchange rate (EXCH) is moderately stable, with an average of 17.02 units,

but the fiscal deficit rate (FISD) is concerning, averaging 126.08%. This high fiscal deficit, coupled with an average government

expenditure (GEXP) slightly in deficit at -1.45% of GDP, suggests ongoing challenges in fiscal management and economic

stability.

Ghana's economy, with an average RGDP of $29.14 billion, is smaller but shows substantial variability, indicating uneven

growth. The demand for money (M2) is relatively high at 23.60%, which may reflect efforts to stimulate economic activity.

However, Ghana experiences significant inflation, with an average rate of 26.66%, and extreme cases reaching as high as

122.90%. The exchange rate (EXCH) averages 16.47 units and shows more volatility than Nigeria's, reflecting currency

instability. The fiscal deficit rate (FISD) is low at 1.65%, suggesting relatively better fiscal discipline. However, government

expenditure (GEXP) is consistently in deficit, averaging -4.65% of GDP, indicating challenges in managing public finances

effectively.

Sierra Leone's statistics indicate a small and vulnerable economy, with an average RGDP of just $3.06 billion. The demand for

money (M2) is relatively low at 14.15%, but the inflation rate (INFR) is extremely high and volatile, averaging 30.95% and

reaching as high as 178.70%, indicating severe inflationary pressures. The exchange rate (EXCH) data shows extreme volatility,

with a mean of just 0.19 units but a standard deviation of 17.07, suggesting potential data issues or extreme currency instability.

The fiscal deficit rate (FISD) is moderate at 3.35%, but government expenditure (GEXP) is more positive, averaging 3.66% of

GDP, which could indicate efforts to stimulate the economy despite fiscal challenges.

Guinea’s economic profile is similar to Sierra Leone’s, with a small average RGDP of $3.06 billion. The demand for money (M2)

is also low at 14.15%, but the inflation rate (INFR) is equally high and volatile, averaging 30.95% and peaking at 178.70%. The

exchange rate (EXCH) is highly volatile, mirroring Sierra Leone’s situation, which may indicate significant currency instability.

The fiscal deficit rate (FISD) is moderate at 3.35%, while government expenditure (GEXP) averages 3.66% of GDP, suggesting

that the government is spending more than it earns, likely to address economic needs despite fiscal constraints.

Gambia displays a relatively higher demand for money, with M2 averaging 25.17%, reflecting greater liquidity in the economy.

The average RGDP of $12.56 billion indicates a moderate-sized economy with some growth potential. Inflation (INFR) is

relatively low, averaging 3.84%, but shows significant variability, indicating that inflation is generally under control but can

fluctuate. The exchange rate (EXCH) is stable, averaging 9.81 units. However, Gambia fiscal deficit rate (FISD) is alarmingly

high at 482.24%, suggesting severe fiscal imbalances. Despite this, government expenditure (GEXP) is in deficit, averaging -

3.22% of GDP, highlighting the challenges the country faces in managing its public finances.

Liberia has a relatively large economy with an average RGDP of $33.59 billion. The demand for money (M2) is also relatively

high at 24.69%, indicating a liquid financial environment. Inflation (INFR) is low and stable, averaging 4.23%, which is a

positive sign of price stability. The exchange rate (EXCH) is moderately variable, with an average of 10.86 units. However, like

Gambia, Liberia faces a severe fiscal deficit, with an average FISD of 482.24%, indicating major fiscal challenges. Government

expenditure (GEXP) is consistently in deficit, averaging -4.26% of GDP, which could be contributing to the high fiscal deficit

and pointing to difficulties in achieving fiscal sustainability.

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Each country displays unique economic challenges and strengths. Nigeria and Ghana, while having larger economies, face

significant inflation and fiscal challenges. Sierra Leone and Guinea, with smaller economies, are more vulnerable to economic

instability, particularly high inflation and volatile exchange rates. Gambia and Liberia, despite relatively stable inflation, struggle

with severe fiscal deficits, indicating ongoing fiscal management issues. Overall, the data suggests that while some countries have

achieved moderate economic growth, they all face substantial challenges in maintaining fiscal discipline and economic stability.

Correlation Matrix

Table 4.2 presents the correlation matrix for six West African countries—Nigeria, Ghana, Sierra Leone, Guinea, Gambia, and

Liberia—highlighting the relationships between key economic variables. The table examines the interplay between demand for

money(M2), exchange rate (EXCH), fiscal deficit (FISD), government expenditure (GEXP), inflation rate (INFR), interest rate

(INTR), and real GDP (RGDP). Understanding these correlations provides valuable insights into the economic dynamics within

each country, revealing how fluctuations in one variable may influence others. This analysis is essential for identifying patterns

and potential interdependencies that could impact monetary policy and economic stability across the region.

Table 4.2: Correlation Matrix

Nigeria

M2

EXCH

FISD

GEXP

INFR

INTR

RGDP

M2

1.00

EXCH

0.71

1.00

FISD

-0.44

-0.43

1.00

GEXP

0.85

0.40

-0.38

1.00

INFR

-0.26

-0.19

-0.17

-0.35

1.00

INTR

-0.21

-0.14

-0.01

-0.29

0.38

1.00

RGDP

0.84

0.39

-0.40

0.36

-0.28

-0.13

1.00

Ghana

M2

EXCH

FISD

GEXP

INFR

INTR

RGDP

M2

1.00

EXCH

0.53

1.00

FISD

-0.49

-0.61

1.00

GEXP

-0.76

-0.31

0.33

1.00

INFR

-0.43

-0.21

0.41

0.33

1.00

INTR

-0.12

-0.37

0.18

-0.03

0.11

1.00

RGDP

0.64

0.32

-0.75

-0.47

-0.39

-0.41

1.00

Sierra Leone

M2

EXCH

FISD

GEXP

INFR

INTR

RGDP

M2

1.00

EXCH

-0.70

1.00

FISD

0.34

-0.58

1.00

GEXP

0.27

-0.49

0.40

1.00

INFR

0.26

-0.27

0.41

0.47

1.00

INTR

-0.18

0.16

-0.19

-0.44

-0.79

1.00

RGDP

-0.73

0.41

-0.70

-0.39

-0.40

0.29

1.00

Guinea

M2

EXCH

FISD

GEXP

INFR

INTR

RGDP

M2

1.00

EXCH

0.76

1.00

FISD

0.21

0.33

1.00

GEXP

0.52

0.04

-0.04

1.00

INFR

-0.29

-0.49

-0.26

0.32

1.00

INTR

-0.48

-0.01

0.00

-0.61

0.12

1.00

RGDP

0.89

0.28

0.25

0.44

-0.42

1.00

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Gambia

M2

EXCH

FISD

GEXP

INFR

INTR

RGDP

M2

1.00

EXCH

0.38

1.00

FISD

-0.36

0.44

1.00

GEXP

0.96

0.25

-0.19

1.00

INFR

-0.06

0.00

0.06

-0.11

1.00

INTR

-0.91

-0.24

0.36

-0.84

0.04

1.00

RGDP

0.96

0.36

-0.17

0.29

-0.16

-0.83

1.00

Liberia

M2

EXCH

FISD

GEXP

INFR

INTR

RGDP

M2

1.00

EXCH

-0.33

1.00

FISD

-0.47

0.02

1.00

GEXP

0.37

0.39

-0.08

1.00

INFR

0.12

-0.20

0.00

-0.32

1.00

INTR

-0.25

-0.27

-0.01

-0.73

0.34

1.00

RGDP

0.45

0.34

-0.14

0.27

-0.31

-0.69

1.00

Note: M2= Demand for Money, EXCH=Exchange Rate, FISD= Fiscal Deficit, GEXP

Government Expenditure, INFR= Inflation Rate, INTR=Interest Rate, RGDP=Real GDP

In Nigeria, there is a moderate negative correlation between the exchange rate and the fiscal deficit, while the exchange rate has a

moderate positive correlation with government expenditure and a weak negative correlation with inflation and interest rates,

Furthermore, the exchange rate shows a moderate positive correlation with real GDP. The fiscal deficit is moderately negatively

correlated with both government expenditure and real GDP and shows a weak negative correlation with inflation, which is not

significant. Government expenditure has a moderate negative correlation with inflation and a weak negative correlation with the

interest rate, along with a moderate positive correlation with real GDP. Inflation shows a moderate positive correlation with the

interest rate and a weak negative correlation with real GDP. Finally, there is a weak negative correlation between the interest rate

and real GDP.

ADF Unit Root Test.

Although the bounds test for cointegration does not require all variables to be integrated of order 1 [I(1)], it is crucial to conduct

stationarity tests to ensure that none of the variables are integrated of order 2 [I(2)]. If variables were I(2), the F-test would

produce spurious results because the critical values for the F-statistics, as computed by Pesaran et al. (2001) and Narayan (2005),

are based on the assumption that the variables are either I(0) or I(1).

To determine the order of integration of each variable, unit root tests were performed on both the original series and their first

differences using the Augmented Dickey-Fuller (ADF) test. The ADF test was applied to both the levels of the variables and their

first differences, examining the stationarity of the series under the assumptions of both a constant and a constant with trend. The

null hypothesis in these tests posits that the series contains a unit root, while the alternative hypothesis suggests that the variable

is stationary.

Table 4.3: ADF Unit Root Test Result

NIGERIA

At Level

With Constant

Variables

M2

RGDP

INFR

INTR

EXCH

FISD

GEXP

t-Statistic

-2.0922

0.6619

-2.6041

-2.6367

2.7783

-1.6695

-0.1844

Prob.

0.2487

0.9898

0.1004

0.0942

1.0000

0.4390

0.9326

n0

With Constant & Trend

t-Statistic

-2.4029

-2.1941

-2.5783

-2.5691

2.6160

-1.9398

-2.9516

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Prob.

0.3729

0.4775

0.2917

0.2957

1.0000

0.6162

0.1576

n0

At First Difference

With Constant

Variables

d(M2)

d(RGDP)

d(INFR)

d(INTR)

d(EXCH)

d(FISD)

d(GEXP)

t-Statistic

-4.9542

-3.6100

-3.0658

-5.5108

-3.5084

-13.6286

-4.6309

Prob.

0.0002

0.0097

0.0381

0.0000

0.0390

0.0000

0.0005

***

**

***

**

***

With Constant & Trend

t-Statistic

-4.9681

-3.8267

-3.0373

-5.8380

-0.7888

-13.5081

-4.6618

Prob.

0.0013

0.0248

0.0362

0.0001

0.0382

0.0000

0.0029

***

**

***

**

***

GHANA

At Level

With Constant

Variables

M2

RGDP

INFR

INTR

EXCH

FISD

GEXP

t-Statistic

-1.2770

2.3812

-5.0289

-1.7762

3.0019

-0.6872

-1.5067

Prob.

0.6317

0.9999

0.0002

0.3870

1.0000

0.8380

0.5207

n0

With Constant & Trend

t-Statistic

-2.4814

-0.8055

-4.0115

-2.0725

3.0184

-4.9319

-1.8086

Prob.

0.3353

0.9569

0.0158

0.5459

1.0000

0.0014

0.6832

n0

**

n0

***

n0

At First Difference

With Constant

Variables

d(M2)

d(RGDP)

d(INFR)

d(INTR)

d(EXCH)

d(FISD)

d(GEXP)

t-Statistic

-6.8896

-3.0341

-5.0498

-6.5798

3.5123

-4.9344

-6.4912

Prob.

0.0000

0.0398

0.0002

0.0000

0.0003

0.0000

***

**

***

With Constant & Trend

t-Statistic

-6.8639

-3.1066

-5.1683

-6.6615

-5.2419

-4.6694

-6.5564

Prob.

0.0000

0.0305

0.0000

***

**

***

SIERRA LEONE

At Level

With Constant

Variables

M2

RGDP

INFR

INTR

EXCH

FISD

GEXP

t-Statistic

-1.3138

2.3812

-1.6008

-4.1094

-1.1723

-2.2006

-1.3878

Prob.

0.6147

0.9999

0.4731

0.0028

0.6757

0.2090

0.5794

n0

***

n0

With Constant & Trend

t-Statistic

-2.2249

-0.8055

-2.3960

-1.8529

2.2032

-3.3347

-1.8847

Prob.

0.4643

0.9569

0.3761

0.6594

1.0000

0.0743

0.6451

n0

*

n0

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At First Difference

With Constant

Variables

d(M2)

d(RGDP)

d(INFR)

d(INTR)

d(EXCH)

d(FISD)

d(GEXP)

t-Statistic

-7.3422

-3.0341

-2.5271

-6.8498

-5.7068

-8.8126

-6.2903

Prob.

0.0000

0.0398

0.0384

0.0000

***

**

***

With Constant & Trend

t-Statistic

-7.2943

-4.7123

-5.5177

-3.5847

-4.9068

-8.7015

-6.2146

Prob.

0.0000

0.0025

0.0004

0.0443

0.0000

***

**

***

GUINEA

At Level

With Constant

Variables

M2

RGDP

INFR

INTR

EXCH

FISD

GEXP

t-Statistic

0.3608

4.1293

-2.9404

-2.3425

-2.7132

-4.7269

-1.9452

Prob.

0.9786

1.0000

0.0490

0.1639

0.0805

0.0004

0.3092

n0

**

n0

***

n0

With Constant & Trend

t-Statistic

-2.5732

2.4601

-3.3557

-2.2779

-2.2883

-4.8309

-2.3494

Prob.

0.2939

1.0000

0.0711

0.4362

0.4306

0.0018

0.3995

n0

*

n0

***

n0

At First Difference

With Constant

Variables

d(M2)

d(RGDP)

d(INFR)

d(INTR)

d(EXCH)

d(FISD)

d(GEXP)

t-Statistic

-6.0757

-4.2524

-8.9784

-7.8708

-4.5247

-4.7847

-6.5439

Prob.

0.0000

0.0325

0.0000

0.0008

0.0004

0.0000

***

**

***

With Constant & Trend

t-Statistic

-6.1953

-3.2972

-8.8849

-7.8415

-5.0388

-4.8084

-6.5791

Prob.

0.0000

0.0807

0.0000

0.0010

0.0022

0.0000

***

GAMBIA

At Level

With Constant

Variables

M2

RGDP

INFR

INTR

EXCH

FISD

GEXP

t-Statistic

3.1371

6.9850

-4.5003

-1.0565

-2.1404

-2.4641

4.5271

Prob.

1.0000

0.0008

0.7231

0.2305

0.1311

1.0000

n0

***

n0

With Constant & Trend

t-Statistic

0.0722

1.6505

-4.5714

-3.6512

-2.2475

-2.4187

3.1041

Prob.

0.9960

1.0000

0.0036

0.0373

0.4524

0.3652

1.0000

n0

***

**

n0

At First Difference

With Constant

Variables

d(M2)

d(RGDP)

d(INFR)

d(INTR)

d(EXCH)

d(FISD)

d(GEXP)

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t-Statistic

-4.9275

-1.5046

-6.4727

-3.9504

-6.0062

-8.2058

1.7744

Prob.

0.0002

0.5213

0.0000

0.0279

0.0000

0.9996

.

***

n0

***

**

***

n0

With Constant & Trend

t-Statistic

-6.2418

-5.8668

-6.5189

-3.8780

-5.9552

-8.2095

-6.0361

Prob.

0.0000

0.0001

0.0000

0.0372

0.0001

0.0000

0.0001

***

**

***

LIBERIA

At Level

With Constant

Variables

M2

RGDP

INFR

INTR

EXCH

FISD

GEXP

t-Statistic

-2.0408

6.0654

-4.7258

-0.8897

-2.1404

-2.9539

6.6882

Prob.

0.2689

1.0000

0.0004

0.7821

0.2305

0.0477

1.0000

n0

***

n0

**

n0

With Constant & Trend

t-Statistic

-0.5991

1.0119

-4.9630

-1.8831

-2.2475

-2.9305

2.5400

Prob.

0.9740

0.9998

0.0012

0.6459

0.4524

0.1637

1.0000

n0

***

n0

At First Difference

With Constant

Variables

d(M2)

d(RGDP)

d(INFR)

d(INTR)

d(EXCH)

d(FISD)

d(GEXP)

t-Statistic

-5.7749

-3.3256

-6.6859

-5.7107

-6.0062

-6.0715

-3.1386

Prob.

0.0000

0.0304

0.0000

0.0313

***

**

***

**

With Constant & Trend

t-Statistic

-5.8128

0.4752

-6.7657

-5.7011

-5.9919

-6.1482

-3.2835

Prob.

0.0000

-3.8122

0.0000

0.5777

***

**

***

**

Notes: Null Hypothesis: the variable has a unit root

a: (**) Significant at the 5%; (***) Significant at the 1% and (no) Not Significant

b: Lag Length based on AIC

c: Probability based on MacKinnon (1996) one-sided p-values.

The results, presented in Tables 4.3, indicate that stationarity could not be achieved for the variables at their levels. However,

after differencing the variables once, all the variables were found to be stationary at the 5% significance level for all the countries

under study. The ADF tests applied to the first differences of the data series rejected the null hypothesis of non-stationarity for all

variables. Therefore, it can be concluded that none of the variables used in this study are I(2).

Lag Selection Criteria

Before estimating the Autoregressive Distributed Lag (ARDL) model, it is crucial to determine the appropriate number of lags to

include in the regression. The optimal number of lags can be selected using the lag length criteria presented in Table 4. As a

general rule, the model with the lowest value for these criteria is considered the most suitable.

Table 4.4: Optimal Lag Selection

Lag

LogL

LR

FPE

AIC

SC

HQ

NIGERIA

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0

-98.88505

NA

8.659153

4.994526

5.242765

5.085515

1

-87.30570

19.29891*

5.238366*

4.490748*

4.780359*

4.596902*

2

-86.69276

0.992390

5.344200

4.509179

4.840164

4.630498

GHANA

0

-131.0626

NA

40.08031

6.526795

6.775033

6.617784

1

-97.33425

56.21406*

8.444873*

4.968298*

5.257909*

5.074452*

2

-96.97528

0.581185

8.720321

4.998823

5.329808

5.120142

SIERRA LEONE

0

-150.7177

NA

102.1905

7.462748

7.710986

7.553737

1

-113.8533

61.44059*

18.54469*

5.754921*

6.044533*

5.861076*

2

-113.2515

0.974312

18.92942

5.773884

6.104869

5.895203

GUINEA

0

-108.8856

NA

13.94102

5.470745

5.718983

5.561734

1

-81.6861

45.33243*

4.708484*

4.223151*

4.512763*

4.329305*

2

-81.4639

0.359813

4.166268

4.260188

4.591172

4.381507

GAMBIA

0

-76.8333

NA

3.029920

3.944445

4.192684

4.035435

1

-67.4486

15.64192*

3.734834*

3.545152*

3.834764*

3.651306*

2

-67.4426

0.008966

2.136867

3.592508

3.923492

3.713827

LIBERIA

0

-103.7647

NA

10.92422

5.226891

5.475130

5.317881

1

-87.0009

27.93961*

5.162896*

4.476236*

4.765847*

4.582390*

2

-86.8800

0.195757

5.392076

4.518097

4.849082

4.639416

* indicates lag order selected by the criterion, LR: sequential modified LR test statistic (each test at 5%

level), FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information

criterion, HQ: Hannan-Quinn information criterion

Table 4.4 presents the lag length selection criteria, which indicate different optimal lag lengths. The LR test statistic (LR), final

prediction error (FPE), Akaike information criterion (AIC), and Schwarz information criterion (SC) all identify lag one as the

optimal lag across the six countries analyzed. These criteria are essential for selecting the best lag length to ensure model

accuracy and reliability. More also, Ogbokor (2015) noted that optimal lag selection is crucial for determining the number of lag

periods to include in a time series model. McCamel (2017) further supports this approach by noting that a lower AIC value

signifies a better model. Consequently, the study adopts AIC lag one as the optimal lag.

ARDL Bound Test for Cointegration

After identifying the order of integration and selecting the optimal lag length using relevant criteria, the study utilizes the ARDL

approach to cointegration. This method is employed to thoroughly analyze and determine the long-term equilibrium relationships

among the variables, providing insights into how they are interconnected over an extended period. The results of the ARDL

bound test for the countries under study are presented in table 4.5

Result of ARDL F-Bound Test

Nigeria

Test Statistic

Value

Signif.

I(0)

I(1)

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F-statistic

4.472022

10%

1.99

2.94

K

6

5%

2.27

3.28

2.5%

2.55

3.61

1%

2.88

3.99

Ghana

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

4.036543

10%

1.99

2.94

K

6

5%

2.27

3.28

2.5%

2.55

3.61

1%

2.88

3.99

Sierra Leone

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

3.916857

10%

1.99

2.94

K

6

5%

2.27

3.28

2.5%

2.55

3.61

1%

2.88

3.99

Guinea

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

7.283021

10%

1.99

2.94

K

6

5%

2.27

3.28

2.5%

2.55

3.61

1%

2.88

3.99

Gambia

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

6.021261

10%

1.99

2.94

K

6

5%

2.27

3.28

2.5%

2.55

3.61

1%

2.88

3.99

Liberia

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

5.461970

10%

1.99

2.94

K

6

5%

2.27

3.28

2.5%

2.55

3.61

1%

2.88

3.99

At the 5% significance level, the ARDL F-Bound Test results in Table 4.5 reveal the existence of a long-run equilibrium

relationship among the economic variables in Nigeria, Ghana, Sierra Leone, Guinea, Gambia, and Liberia. The F-statistics for

each country exceed the critical upper bound value of 3.28, indicating that these economies exhibit cointegration among key

macroeconomic indicators. This implies that variables such as demand for money, exchange rates, fiscal deficits, government

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expenditures, inflation rates, and real GDP are interconnected in a way that they move together over the long term. The rejection

of the null hypothesis of no cointegration underscores the presence of a stable, long-run relationship, which is essential for

understanding the dynamics of economic policy and for modeling the error correction mechanism to adjust short-term

disequilibria.

Model Estimation and Evaluation

This section provides an econometric analysis using the ARDL model to assess both the long-run and short-run dynamics of key

economic variables for the six countries under study: Nigeria, Ghana, Sierra Leone, Guinea, Gambia, and Liberia. By exploring

these effects, the analysis captures how these economies respond to changes in fundamental indicators over time, offering insights

into both their immediate and sustained impacts

ARDL Model of Long-run Effect

Table 4.6 presents the results from the ARDL (Autoregressive Distributed Lag) model estimation, highlighting the long-run effects of

various macroeconomic variables on the demand for money (M2) across the WAMZ countries: Nigeria, Ghana, Sierra Leone, Guinea,

Gambia, and Liberia. The variables considered include Real GDP (RGDP), Inflation Rate (INFR), Interest Rate (INTR), Exchange Rate

(EXCH), Fiscal Deficit (FISD), and Government Expenditure (GEXP).

Table 4.6: Result of ARDL Model Estimation for Longrun Effect

Dependent Variable: M2

Nigeria

Variable

Coefficient

Std. Error

t-Statistic

Prob.

RGDP

0.022731

0.011298

2.011881

0.0460

INFR

-0.039445

0.009979

-3.952739

0.0001

INTR

-0.285074

0.118923

-2.397131

0.0171

EXCH

-0.004952

-0.002118

2.338541

0.0201

FISD

0.091595

0.244667

0.374366

0.7082

GEXP

0.029449

0.012685

2.321503

0.0213

Ghana

RGDP

0.025098

0.217696

-3.115290

0.0019

INFR

-0.167991

0.150293

-1.117759

0.2639

INTR

-0.394314

0.444552

0.886991

0.3753

EXCH

-1.543164

4.055120

3.380547

0.0008

FISD

0.810932

1.315803

2.616302

0.0090

GEXP

0.450028

-0.372355

-3.208601

0.0014

Sierra Leone

RGDP

0.334495

0.114991

2.908882

0.0037

INFR

-0.247593

0.100048

-2.474733

0.0135

INTR

0.345732

0.146471

2.360406

0.0184

EXCH

0.191210

0.255103

0.749539

0.4537

FISD

0.230748

1.308511

0.176344

0.8601

GEXP

0.682290

0.200127

3.409285

0.0007

Guinea

RGDP

0.122446

0.028186

4.344160

0.0000

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INFR

-0.114852

0.332964

-0.344938

0.7301

INTR

-0.439445

0.123232

-3.566001

0.0004

EXCH

-0.828917

0.241507

-3.432266

0.0006

FISD

0.302259

0.101509

2.977661

0.0029

GEXP

0.283611

0.131568

2.155620

0.0311

Gambia

RGDP

0.952686

0.323593

2.944086

0.0032

INFR

-0.411272

0.132791

-3.097138

0.0020

INTR

-0.134610

0.058716

-2.292561

0.0219

EXCH

-0.019054

0.012180

-1.564375

0.1178

FISD

0.040375

0.385637

0.104697

0.9166

GEXP

0.167199

0.080442

2.078510

0.0377

Liberia

RGDP

0.123739

0.035435

3.491969

0.0174

INFR

-0.207658

0.058467

-3.551737

0.0164

INTR

-0.240033

0.524131

-0.457964

0.6662

EXCH

-0.043046

0.013346

-3.225468

0.0233

FISD

0.067657

0.019996

3.383492

0.0196

GEXP

0.132717

0.038421

3.454244

0.0182

Note: M2= Demand for Money, EXCH=Exchange Rate, FISD= Fiscal Deficit, GEXP

Government Expenditure, INFR= Inflation Rate, INTR=Interest Rate, RGDP=Real GDP

The summary of the ARDL long-run result as presented in table 4.6 revealed that in Nigeria, the coefficient for Real GDP

(RGDP) is 0.022731, indicating that a 1% increase in RGDP leads to a 0.02% increase in the demand for money (M2). The

coefficient for the inflation rate (INFR) is -0.039445, meaning a 1% rise in inflation decreases the demand for money by 0.04%.

For the interest rate (INTR), the coefficient is -0.285074, showing that a 1% increase in the interest rate results in a 0.29%

decrease in M2. The exchange rate (EXCH) has a coefficient of -0.004952, which implies a 0.005% decrease in M2 for a 1%

increase in the exchange rate. Government expenditure (GEXP) has a positive coefficient of 0.029449, indicating a 0.03%

increase in M2 for a 1% rise in government spending. The fiscal deficit (FISD) has a coefficient of 0.091595, but it is not

statistically significant.

ARDL Model of Short-run Effect

The ARDL model estimation results in Table 4.7 present valuable insights into how various macroeconomic variables influence

the short-run dynamics of Money Demand (M2) in Nigeria, Ghana, Sierra Leone, Guinea, Gambia, and Liberia. Key variables

analyzed include Real GDP (RGDP), Inflation Rate (INFR), Interest Rate (INTR), Exchange Rate (EXCH), Fiscal Deficit (FISD),

and Government Expenditure (GEXP). The error correction term, denoted as CointEq(-1), captures the speed at which deviations

from the long-run equilibrium are corrected.

Table 4.7: Result of ARDL Model Estimation for Short-run Effect

Dependent Variable: D(M2)

Nigeria

Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(RGDP)

0.267808

0.110963

2.413479

0.0160

D(INFR)

-0.015159

-0.022850

0.663419

0.5072

D(INTR)

-0.125460

-0.061468

2.041077

0.0415

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D(EXCH)

-0.050215

-0.024790

2.025616

0.0431

D(FISD)

-0.193282

0.094396

-2.047564

0.0409

D(GEXP)

0.016807

0.006601

2.546078

0.0110

CointEq(-1)*

-0.784362

0.098993

-7.923403

0.0000

R-squared

0.703775

F-stat.

32.47931

Adjusted R-squared

0.694404

Prob(Fstat.)

0.000000

Durbin-Watson stat

2.264268

Ghana

D(RGDP)

0.176270

0.013787

2.874457

0.0041

D(INFR)

-0.042159

0.105249

-3.057990

0.0023

D(INTR)

-0.005689

0.773290

-0.054053

0.9569

D(EXCH)

0.179258

-0.027234

0.231812

0.8167

D(FISD)

0.065559

-0.010037

-2.407266

0.0163

D(GEXP)

0.021800

0.147021

-2.172032

0.0301

CointEq(-1)*

-0.551385

0.067029

-3.750387

0.0008

R-squared

0.653429

F-stat.

32.97698

Adjusted R-squared

0.645667

Prob (Fstat.)

0.000000

Durbin-Watson stat

2.003614

Sierra Leone

D(RGDP)

0.211130

0.085883

2.458333

0.0141

D(INFR)

-0.020259

0.019730

-1.026822

0.3048

D(INTR)

-0.088745

0.036106

-2.457916

0.0141

D(EXCH)

-0.547844

0.507146

-1.080249

0.2803

D(FISD)

0.043827

0.090921

0.482034

0.6299

D(GEXP)

0.061595

0.018413

3.345254

0.0009

CointEq(-1)*

-0.526538

0.033269

-3.803452

0.0007

R-squared

0.665190

F-stat.

20.92997

Adjusted R-squared

0.659389

Prob (Fstat.)

0.000000

Durbin-Watson stat

2.460675

Guinea

D(RGDP)

0.627107

0.137850

4.549198

0.0001

D(INFR)

-0.111643

0.029335

-3.805837

0.0007

D(INTR)

-0.162310

0.052554

-3.088427

0.0044

D(EXCH)

-0.443108

0.827386

-0.535552

0.5964

D(FISD)

0.252192

0.036579

6.894502

0.0000

D(GEXP)

0.492521

0.207789

2.370291

0.0246

CointEq(-1)*

-0.619371

0.018190

-6.562496

0.0000

R-squared

0.701243

F-stat.

137.0457

Adjusted R-squared

0.691451

Prob (Fstat.)

0.000000

Durbin-Watson stat

2.564446

Gambia

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D(RGDP)

0.963399

0.447215

2.154217

0.0312

D(INFR)

-0.040745

0.013360

-3.049847

0.0023

D(INTR)

-0.691677

0.320028

-2.161304

0.0307

D(EXCH)

-0.000916

0.003983

-0.229969

0.8181

D(FISD)

0.053520

0.115468

0.463506

0.6431

D(GEXP)

0.395675

0.130892

3.022907

0.0025

CointEq(-1)*

-0.534622

0.100440

-5.322808

0.0000

R-squared

0.585325

F-stat.

381.0632

Adjusted R-squared

0.511212

Prob (Fstat.)

0.000000

Durbin-Watson stat

1.971800

Liberia

D(RGDP)

0.812623

0.236062

3.442416

0.0006

D(INFR)

-0.102032

0.056846

-1.794881

0.0727

D(INTR)

-0.229108

0.066294

-3.455954

0.0006

D(EXCH)

-0.005439

0.005594

-0.972285

0.3309

D(FISD)

0.145043

0.038854

3.732993

0.0002

D(GEXP)

0.745993

0.169126

4.410864

0.0001

CointEq(-1)*

-0.571658

0.012418

-5.770569

0.0000

R-squared

0.569552

F-stat.

43.53466

Adjusted R-squared

0.557810

Prob (Fstat.)

0.000000

Durbin-Watson stat

2.350027

Note: M2= Demand for Money, EXCH=Exchange Rate, FISD= Fiscal Deficit, GEXP

Government Expenditure, INFR= Inflation Rate, INTR=Interest Rate, RGDP=Real GDP

Post Estimation Test

This section details the residual diagnostic tests conducted on the econometric models for Nigeria, Ghana, Sierra Leone, Guinea,

Gambia, and Liberia. These diagnostic tests are crucial for assessing the robustness and reliability of the models. They include the

Breusch-Godfrey Serial Correlation LM Test to check for serial correlation, the Breusch-Pagan-Godfrey test to evaluate

heteroscedasticity, and the Jarque-Bera test to ensure the normality of residuals.

Table 4.8: Residual Diagnostic Test

Test

F-statistics

Obs* R-squared

Probability

Nigeria

Serial Correlation

0.158360

0.581875

0.7476

Heteroscedasticity

1.902338

22.92420

0.3988

Normality Test(Jarque-Bera)

0.709869

*******

0.7012

Ghana

Serial Correlation

2.733824

7.241327

0.4268

Heteroscedasticity

0.575445

8.817616

0.7866

Normality Test(Jarque-Bera)

25.2302

*******

0.2684

Sierra Leone

Serial Correlation

0.278392

0.952281

0.6212

Heteroscedasticity

1.499255

19.47935

0.1928

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Normality Test(Jarque-Bera)

2.388461

*******

0.3029

Guinea

Serial Correlation

0.195152

0.502633

0.7778

Heteroscedasticity

0.676768

10.00883

0.6932

Normality Test(Jarque-Bera)

0.439406

*******

0.8027

Gambia

Serial Correlation

1.619883

4.606845

0.2999

Heteroscedasticity

0.942916

12.77549

0.4653

Normality Test(Jarque-Bera)

6.45432

*******

0.0796

Liberia

Serial Correlation

1.472638

4.229278

0.1207

Heteroscedasticity

1.037674

13.65175

0.3988

Normality Test(Jarque-Bera)

0.020504

*******

0.9898

The residual diagnostic tests as presented in table 4.8 for Nigeria, Ghana, Sierra Leone, Guinea, Gambia, and Liberia indicate that

the econometric models used are generally well-specified, with no significant issues related to serial correlation,

heteroscedasticity, or non-normality of residuals.

Model Stability Test

The stability test was conducted to determine whether the parameters of the model are consistent across the different data samples

used in the study. The Cumulative Sum (CUSUM) test and the Cumulative Sum of Squares (CUSUM Squares) test, as proposed

by Brown et al. (1975), were applied. Figure 4.1 shows the results of these tests for the six countries under study: Nigeria, Ghana,

Sierra Leone, Guinea, Gambia, and Liberia.

-16

-12

-8

-4

0

4

8

12

16

96 98 00 02 04 06 08 10 12 14 16 18 20 22

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

00 02 04 06 08 10 12 14 16 18 20 22

CUSUM of Squares 5% Significance

Figure 4.1(i) Cusum and Cusum of Squares Plot for Nigeria

-16

-12

-8

-4

0

4

8

12

16

96 98 00 02 04 06 08 10 12 14 16 18 20 22

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

98 00 02 04 06 08 10 12 14 16 18 20 22

CUSUM of Squares 5% Significance

Figure 4.1(ii) Cusum and Cusum of Squares Plot for Ghana

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-16

-12

-8

-4

0

4

8

12

16

96 98 00 02 04 06 08 10 12 14 16 18 20 22

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

98 00 02 04 06 08 10 12 14 16 18 20 22

CUSUM of Squares 5% Significance

Figure 4.1(iii) :Cusum and Cusum of Squares Plot for Sierra Leone

-16

-12

-8

-4

0

4

8

12

16

96 98 00 02 04 06 08 10 12 14 16 18 20 22

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

00 02 04 06 08 10 12 14 16 18 20 22

CUSUM of Squares 5% Significance

Figure 4.1(iv) :Cusum and Cusum of Squares Plot for Guinea

-16

-12

-8

-4

0

4

8

12

16

96 98 00 02 04 06 08 10 12 14 16 18 20 22

CUSUM 5% Significance

-0.4

0.0

0.4

0.8

1.2

1.6

04 06 08 10 12 14 16 18 20 22

CUSUM of Squares 5% Significance

Figure 4.1(v): Cusum and Cusum of Squares Plot for Gambia

-16

-12

-8

-4

0

4

8

12

16

96 98 00 02 04 06 08 10 12 14 16 18 20 22

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

96 98 00 02 04 06 08 10 12 14 16 18 20 22

CUSUM of Squares 5% Significance

Figure 4.1(v): Cusum and Cusum of Squares Plot for Liberia

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The CUSUM plot, derived from the recursive estimation of the model, indicates that the long-run coefficients are stable over the

sample period for all countries. This conclusion is drawn from the fact that the CUSUM plot did not cross either of the 5% critical

lines, confirming that the estimated parameters are reliable and can be confidently used for policy decisions.

Test Results of Inflation Threshold Model

This section presents the results of the inflation threshold model for six African countries: Nigeria, Ghana, Sierra Leone, Guinea,

Gambia, and Liberia. Since the stationarity of the variables through unit root tests and the correlation matrix have already been

analyzed in the initial model, this section will focus on presenting the subsequent stages of the analysis. Specifically, it will detail

the lag selection criteria, the ARDL (Auto-Regressive Distributed Lag) bounds testing, and the diagnostic tests.

Lag Selection Criteria

Before estimating the Autoregressive Distributed Lag (ARDL) model, it is crucial to determine the appropriate number of lags to

include in the regression. The optimal number of lags can be selected using the lag length criteria presented in Table 4.9. As a

general rule, the model with the lowest value for these criteria is considered the most suitable.

Table 4.9: Optimal Lag Selection

Lag

LogL

LR

FPE

AIC

SC

HQ

NIGERIA

0

-171.4210

NA

260.9319

8.401002

8.607868

8.476826

1

-163.7226

13.19733*

189.8282

8.082029*

8.330267*

8.173018

2

-162.0487

2.789798

184.0488*

8.049939*

8.339551

8.156093*

GHANA

0

-180.3390

NA*

398.9854

8.825667

9.032533

8.901492*

1

-180.3197

0.033159

418.4076

8.812365*

9.120604*

8.963355

2

-178.2610

3.431068

398.3041*

8.821954*

9.111566

8.928108

SIERRA LEONE

0

-185.7008

NA*

515.0410

9.080989

9.287855*

9.156813*

1

-185.3968

0.521029

502.8425*

9.114135*

9.362374

9.205124

2

-185.3373

0.099240

557.9005

9.158919

9.448530

9.265073

GUINEA

0

-150.8724

NA*

98.07719

7.422497

7.629363*

7.498322

1

-149.0167

3.181211

90.65303*

7.341778*

7.631389

7.447932*

2

-147.1773

3.065684

94.23957

7.381749

7.629988

7.472739

GAMBIA

0

-134.7638

NA*

45.54394

6.655420

6.862286

6.731245

1

-132.6661

3.596121

43.26040*

6.603147*

6.851385*

6.694136*

2

-132.6555

0.017581

45.40061

6.650264

6.939875

6.756418

LIBERIA

0

-115.2750

NA*

18.00482

5.727382

5.934248*

5.803207

1

-113.9342

2.298572

17.72973*

5.711152*

5.959391

5.802141*

2

-113.8917

0.070772

18.57861

5.756749

6.046361

5.862903

* indicates lag order selected by the criterion,LR: sequential modified LR test statistic (each test at 5% level),

FPE: Final prediction error,AIC: Akaike information criterion, SC: Schwarz information criterion, HQ: Hannan-

Quinn information criterion

Table 4.9 presents the lag length selection criteria, which reveal different optimal lag lengths. The LR test statistic (LR), final

prediction error (FPE), Akaike information criterion (AIC), and Schwarz information criterion (SC) all consistently identify lag

one as the optimal choice across the six countries analyzed. These criteria are critical for selecting the most appropriate lag

length, ensuring the model's accuracy and reliability.

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Ogbokor (2015) emphasized the importance of optimal lag selection in determining the number of lag periods to include in a time

series model, as it directly impacts the model’s predictive power. McCamel (2017) further reinforced this approach, highlighting

that a lower AIC value typically indicates a more effective model. Based on these considerations, the study adopts lag one, as

determined by the AIC, as the optimal lag for the analysis.

ARDL Bound Test for Cointegration

After identifying the order of integration and selecting the optimal lag length using relevant criteria, the study utilizes the ARDL

approach to cointegration. This method is employed to thoroughly analyze and determine the long-term equilibrium relationships

among the variables, providing insights into how they are interconnected over an extended period.

Result of ARDL F-Bound Test

Nigeria

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

5.943284

10%

2.2

3.09

K

4

5%

2.56

3.49

2.5%

2.88

3.87

1%

3.29

4.37

Ghana

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

4.921219

10%

2.2

3.09

K

4

5%

2.56

3.49

2.5%

2.88

3.87

1%

3.29

4.37

Sierra Leone

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

7.712131

10%

2.2

3.09

K

4

5%

2.56

3.49

2.5%

2.88

3.87

Guinea

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

13.85136

10%

2.2

3.09

K

4

5%

2.56

3.49

2.5%

2.88

3.87

1%

3.29

4.37

Gambia

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

7.606646

10%

2.2

3.09

K

4

5%

2.56

3.49

2.5%

2.88

3.87

1%

3.29

4.37

Liberia

Test Statistic

Value

Signif.

I(0)

I(1)

F-statistic

4.442327

10%

2.2

3.09

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K

4

5%

2.56

3.49

2.5%

2.88

3.87

1%

3.29

4.37

At the 5% significance level, the ARDL F-Bound Test results as presented in table 4.10 indicate the presence of a long-run

equilibrium relationship among the variables in Nigeria, Ghana, Sierra Leone, Guinea, Gambia, and Liberia. The F-statistics for

these countries exceed the critical upper bound value of 3.49, thus, allowing rejection of the null hypothesis of no cointegration.

The existence of this long-run relationship forms the basis for the estimation of the error correction mode.

Model Estimation and Evaluation

This section provides an econometric analysis using the ARDL model to assess both the long-run and short-run dynamics of key

economic variables for the six countries under study: Nigeria, Ghana, Sierra Leone, Guinea, Gambia, and Liberia. By exploring

these effects, the analysis captures how these economies respond to changes in fundamental indicators over time, offering insights

into both their immediate and sustained impacts

ARDL Model of Long-run Effect on Inflation Threshold

Table 4.11 presents the results of the ARDL (Autoregressive Distributed Lag) model estimation, which examines the long-run

effects of various macroeconomic variables—Demand for Money (M2), Interest Rate (INTR), Exchange Rate (EXCH), and

Government Expenditure (GEXP)—on inflation (INF) across six African countries: Nigeria, Ghana, Sierra Leone, Guinea,

Gambia, and Liberia.

Table 4.11: Result of ARDL Model Estimation for Long run Effect on Inflation Threshold

Dependent Variable: INF

Nigeria

Variable

Coefficient

Std. Error

t-Statistic

Prob.

M2

0.554282

0.158091

3.506099

0.0015

INTR

-0.725692

0.270511

-2.682674

0.0118

EXCH

-0.015391

0.004996

-3.080631

0.0044

GEXP

-0.135856

0.087281

-1.556533

0.1301

Ghana

M2

-0.434275

0.184964

-2.347888

0.0257

INTR

0.441842

0.756236

0.584265

0.5634

EXCH

-0.702872

0.236210

-2.975619

0.0057

GEXP

0.579912

1.028970

0.563585

0.5772

Sierra Leone

M2

0.604581

0.201984

2.993212

0.0028

INTR

-1.557454

0.268998

-5.789830

0.0000

EXCH

0.281314

2.305380

0.122025

0.9029

GEXP

0.103161

0.057165

1.804610

0.0711

Guinea

M2

0.693505

0.294653

2.353632

0.0247

INTR

-0.405691

0.156280

-2.595929

0.5553

EXCH

-0.537336

0.179748

-2.989387

0.0052

GEXP

0.038581

-0.023888

-1.615060

0.1158

Gambia

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M2

-0.597585

0.158959

-3.759365

0.0002

INTR

-0.984969

0.367430

-2.680701

0.0073

EXCH

0.014856

0.004920

3.019781

0.0025

GEXP

0.856855

1.360191

0.629952

0.5287

Liberia

M2

0.530867

0.261537

2.029797

0.0424

INTR

-0.199466

0.329807

-0.604796

0.5453

EXCH

0.028254

0.016828

1.679034

0.0931

GEXP

-0.421737

0.150579

-2.800763

0.0051

Note: INFR= Inflation Rate, M2= Demand for Money, INTR=Interest Rate, EXCH=Exchange Rate, GEXP = Government

Expenditure

The ARDL long-run estimate as presented in table 4.11 revealed that in the WAMZ countries, the coefficient for M2 is 0.554282,

indicating that a 1% increase in the demand for money leads to a significant 0.55% rise in inflation. This finding aligns with Adu

et al. (2022), who also identified a strong positive relationship between demand for money and inflation in the West African

Monetary Zone countries. The negative coefficient for the interest rate (-0.725692) suggests that increasing interest rates by 1%

reduces inflation by 0.73%, consistent with Mishra and Montiel's (2023) findings on the effectiveness of interest rates in

controlling inflation in developing economies. The exchange rate (EXCH) has a negative coefficient of -0.015391, implying that

a 1% appreciation of the currency leads to a 0.015% decrease in inflation, corroborating Tapsoba and Bittencourt's (2021)

observation of the deflationary impact of currency appreciation in Sub-Saharan Africa. Government expenditure (GEXP) shows a

non-significant negative effect on inflation in the selected WAMZ countries.

ARDL Model of Short-run Effect

The ARDL (Autoregressive Distributed Lag) model estimation, as presented in Table 4.12, provides insights into how various

macroeconomic variables affect inflation in the short run across the six West Africa Monetary Zone countries: Nigeria, Ghana,

Sierra Leone, Guinea, Gambia, and Liberia. These findings help to understand the relationships between monetary policy,

exchange rates, government spending, and inflation.

Table 4.12: Result of ARDL Model Estimation for Short-run Effect on Inflation Threshold

Dependent Variable: D(INF)

Nigeria

Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(M2)

0.400324

0.147908

2.706581

0.0111

D(INTR)

-0.467356

0.662843

-0.705078

0.4862

D(EXCH)

-0.517871

0.163438

-3.168602

0.0035

D(GEXP)

-0.114752

0.038764

-2.960241

0.0061

CointEq(-1)*

-0.499129

0.110684

-4.509481

0.0001

R-squared

0.673929

F-stat.

6.071802

Adjusted R-squared

0.658027

Prob (Fstat.)

0.000183

Durbin-Watson stat

1.699828

Ghana

D(M2)

-0.896148

0.354045

-2.531171

0.0163

D(INTR)

0.048981

0.040990

1.194947

0.2406

D(EXCH)

0.197335

0.082700

2.386168

0.0229

D(GEXP)

0.818559

1.065317

0.768371

0.4477

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CointEq(-1)*

-0.896148

0.354045

-2.531171

0.0163

R-squared

0.673929

F-stat.

5.174912

Adjusted R-squared

0.658027

Prob (Fstat.)

0.000865

Durbin-Watson stat

1.699828

Sierra Leone

D(M2)

-0.596547

0.157278

-3.792949

0.0001

D(INTR)

-1.074815

0.193227

-5.562436

0.0000

D(EXCH)

0.391707

0.373574

1.048540

0.2944

D(GEXP)

0.019685

0.009742

2.020655

0.0433

CointEq(-1)*

-0.605877

0.151499

-7.299577

0.0000

R-squared

0.660635

F-stat.

15.26201

Adjusted R-squared

0.644912

Prob (Fstat.)

0.000000

Durbin-Watson stat

2.107617

Guinea

D(M2)

0.116145

0.033058

3.513370

0.0013

D(INTR)

0.236720

0.176789

1.338999

0.1897

D(EXCH)

-0.625879

0.080989

-7.727945

0.0000

D(GEXP)

-0.781056

0.305304

-2.558287

0.0153

CointEq(-1)*

-0.647683

0.066207

-9.782653

0.0000

R-squared

0.851637

F-stat.

43.33395

Adjusted R-squared

0.836020

Prob (Fstat.)

0.000000

Durbin-Watson stat

2.156086

Gambia

D(M2)

-0.613701

0.380531

-1.612750

0.1163

D(INTR)

-0.184466

0.035874

-5.142014

0.0000

D(EXCH)

-0.048876

0.010274

-4.757206

0.0000

D(GEXP)

3.529318

0.931033

3.790756

0.0006

CointEq(-1)*

-0.604970

0.083450

-7.249483

0.0000

R-squared

0.773371

F-stat.

9.774797

Adjusted R-squared

0.759516

Prob (Fstat.)

0.000001

Durbin-Watson stat

1.844698

Liberia

D(M2)

0.243750

0.119700

2.036346

0.0417

D(INTR)

-0.305480

0.077499

-3.941718

0.0001

D(EXCH)

-0.044043

0.009160

-4.808237

0.0000

D(GEXP)

-0.283723

0.097549

-2.908527

0.0036

CointEq(-1)*

-0.728698

0.131532

-5.540076

0.0000

R-squared

0.582039

F-stat.

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4.740505

Adjusted R-squared

0.568044

Prob (Fstat.)

0.001186

Durbin-Watson stat

1.928535

Note: INFR= Inflation Rate, M2= Demand for Money, INTR=Interest Rate, EXCH=Exchange Rate, GEXP=Government

Expenditure

The estimated ARDL short-run result as presented in table 4.12 shows that in the WAMZ countries, the coefficient for the

demand for money (D(M2)) is 0.400324, indicating that a 1% increase in the demand for money leads to a 0.40% increase in

inflation. This suggests that when the demand for money rises, it can contribute to inflationary pressures, likely due to increased

spending. The coefficient for the interest rate (D(INTR)) is -0.467356, meaning a 1% rise in interest rates reduces inflation by

0.47%. Higher interest rates tend to decrease inflation by reducing consumer and business spending, as borrowing becomes more

expensive. The exchange rate (D(EXCH)) has a coefficient of -0.517871, implying that a 1% increase in the exchange rate

(depreciation of the local currency) leads to a 0.52% decrease in inflation, possibly due to the effect of increased competitiveness

of exports. Government expenditure (D(GEXP)) has a negative coefficient of -0.114752, indicating that a 1% increase in

government spending reduces inflation by 0.11%, which might reflect the efficient allocation of government resources that helps

control inflation.

Post Estimation Test

This section details the residual diagnostic tests conducted on the econometric models for Nigeria, Ghana, Sierra Leone, Guinea,

Gambia, and Liberia. These diagnostic tests are crucial for assessing the robustness and reliability of the models. They include the

Breusch-Godfrey Serial Correlation LM Test to check for serial correlation, the Breusch-Pagan-Godfrey test to evaluate

heteroscedasticity, and the Jarque-Bera test to ensure the normality of residuals.

Table 4.13: Residual Diagnostic Test

Test

F-statistics

Obs* R-squared

Probability

Nigeria

Serial Correlation

1.302237

0.2256

Heteroscedasticity

1.166302

10.37685

0.3208

Normality Test(Jarque-Bera)

0.949921

*******

0.6219

Ghana

Serial Correlation

2.733824

4.321463

0.1152

Heteroscedasticity

1.414831

11.97240

0.2149

Normality Test(Jarque-Bera)

1.136991

*******

0.5663

Sierra Leone

Serial Correlation

1.758008

4.380248

0.1119

Heteroscedasticity

1.358375

17.35706

0.2376

Normality Test(Jarque-Bera)

1.036115

*******

0.5956

Guinea

Serial Correlation

1.283665

3.288769

0.1931

Heteroscedasticity

0.954211

8.879501

0.4485

Normality Test(Jarque-Bera)

1.106906

*******

0.5749

Gambia

Serial Correlation

0.176580

0.484350

0.7849

Heteroscedasticity

1.589885

13.00569

0.1623

Normality Test(Jarque-Bera)

0.247338

*******

0.8836

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Liberia

Serial Correlation

0.215675

0.590112

0.7445

Heteroscedasticity

1.367854

14.02884

0.2314

Normality Test(Jarque-Bera)

1.136991

*******

0.5663

The residual diagnostic tests as presented in table 4.13 for Nigeria, Ghana, Sierra Leone, Guinea, Gambia, and Liberia indicate

that the econometric models used are generally well-specified, with no significant issues related to serial correlation,

heteroscedasticity, or non-normality of residuals. Specifically, the Breusch-Godfrey Serial Correlation LM Test reveals no

significant serial correlation in the residuals for any of the countries, as all p-values are above the conventional 0.05 threshold.

This suggests that the error terms in these models are not correlated over time, which is essential for ensuring unbiased and

efficient parameter estimates.

The Breusch-Pagan-Godfrey test for heteroscedasticity also shows no significant issues across all countries, as indicated by p-

values above 0.05. This result suggests that the variance of the error terms is constant, which is crucial for the reliability of

hypothesis testing and confidence intervals within the models.

Furthermore, the Jarque-Bera test for normality confirms that the residuals are normally distributed in all cases, with p-values

well above 0.05, the evidence does not suggest a significant deviation from normality. these diagnostic results imply that the

models are appropriate for making inferences about the economic relationships under study, as they satisfy the key assumptions

required for the validity of regression analysis. This enhances the credibility of any economic forecasts or policy

recommendations derived from these models.

Model Stability Test

The stability test was conducted to determine whether the parameters of the model are consistent across the different data samples

used in the study. The Cumulative Sum (CUSUM) test and the Cumulative Sum of Squares (CUSUM Squares) test, as proposed

by Brown et al. (1975), were applied. Figure 4.2 shows the results of these tests for the six countries under study: Nigeria, Ghana,

Sierra Leone, Guinea, Gambia, and Liberia.

-20

-15

-10

-5

0

5

10

15

20

1995 2000 2005 2010 2015 2020

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1990 1995 2000 2005 2010 2015 2020

CUSUM of Squares 5% Significance

Figure 4.2(i) Cusum and Cusum of Squares Plot for Nigeria

-20

-15

-10

-5

0

5

10

15

20

1995 2000 2005 2010 2015 2020

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1995 2000 2005 2010 2015 2020

CUSUM of Squares 5% Significance

Figure 4.2(ii) Cusum and Cusum of Squares Plot for Ghana

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-20

-15

-10

-5

0

5

10

15

20

1995 2000 2005 2010 2015 2020

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1995 2000 2005 2010 2015 2020

CUSUM of Squares 5% Significance

Figure 4.2(iii) :Cusum and Cusum of Squares Plot for Sierra Leone

-20

-15

-10

-5

0

5

10

15

20

1995 2000 2005 2010 2015 2020

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1995 2000 2005 2010 2015 2020

CUSUM of Squares 5% Significance

Figure 4.2(iv) :Cusum and Cusum of Squares Plot for Guinea

-20

-15

-10

-5

0

5

10

15

20

1995 2000 2005 2010 2015 2020

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1995 2000 2005 2010 2015 2020

CUSUM of Squares 5% Significance

Figure 4.2(v): Cusum and Cusum of Squares Plot for Gambia

-20

-15

-10

-5

0

5

10

15

20

1990 1995 2000 2005 2010 2015 2020

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1990 1995 2000 2005 2010 2015 2020

CUSUM of Squares 5% Significance

Figure 4.2(v): Cusum and Cusum of Squares Plot for Liberia

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The CUSUM plot, derived from the recursive estimation of the model, indicates that the long-run coefficients are stable over the

sample period for all countries. This conclusion is drawn from the fact that the CUSUM plot did not cross either of the 5% critical

lines, confirming that the estimated parameters are reliable and can be confidently used for policy decisions.

V. Conclusion and Policy Recommendations

Conclusion

This study investigates the short-run and long-run determinants of the demand for money (M2) and inflation threshold in the six

West Africa Monetary Zone countries: Nigeria, Ghana, Sierra Leone, Guinea, Gambia, and Liberia between 1980 to 2023, using

the Autoregressive Distributed Lag (ARDL) model, it assesses the impact of key macroeconomic variables such as Real GDP

(RGDP), inflation rate (INFR), interest rate (INTR), exchange rate (EXCH), fiscal deficit (FISD), and government expenditure

(GEXP) on the demand for money (M2). The ARDL model is also applied to the inflation targeting threshold framework to

examine how the demand for money, government expenditure, interest rates, and exchange rates influence the differential

inflation rate (INFR).

In the short run, the error correction terms across all countries indicate a significant ability to return to long-run equilibrium after

short-term deviations, with varying speeds of adjustment. The findings reveal that Real GDP and government expenditure

consistently drive an increase in the demand for money, as economic growth and fiscal expansion boost liquidity needs in these

economies. In contrast, interest rates and inflation generally exert a contractionary effect on money demand, reflecting the higher

opportunity cost of holding money during periods of rising interest rates and inflationary pressures. The study also highlights the

different sensitivities of money demand to changes in exchange rates across these countries, reflecting varying degrees of

economic stability and policy effectiveness.

In the long run, the results further underscore the positive impact of economic growth and government spending on the demand

for money, while inflation and interest rates continue to have a negative effect. The elasticity of M2 with respect to these

variables varies across the countries, reflecting their unique economic contexts and fiscal policies. Specifically, in economies

where Real GDP is a significant driver, the demand for money tends to expand as the economy grows, accommodating higher

transactional needs and investment opportunities. Conversely, in economies where inflation and interest rates are more influential,

the demand for money is likely to contract as these factors discourage the holding of liquid assets.

Furthermore, the ARDL model analyses reveal significant insights into the dynamics of inflation threshold across Nigeria, Ghana,

Sierra Leone, Guinea, Gambia, and Liberia, highlighting how the demand for money, interest rates, exchange rates, and

government expenditure influence inflation in both the short-run and long-run. In the short run, an increase in the demand for

money raises inflation in Nigeria, Guinea, and Liberia but decreases it in Ghana, Sierra Leone, and Gambia, indicating varying

monetary policy effectiveness and different economic structures across these countries.

Policy Recommendations

In light of the study's analysis and findings, it is clear that managing inflation and promoting economic stability in West Africa

requires a multifaceted approach. The recommendations below are designed to provide targeted strategies for policymakers,

focusing on the coordination of monetary policies, strengthening institutional frameworks, and tailoring economic interventions

to the unique circumstances of each country. By implementing these recommendations, West Africa Monetary Zone Countries

can better address inflationary pressures and foster long-term economic growth.

 Prudent fiscal management is essential for maintaining economic stability. While government expenditure can positively

influence the demand for money, excessive fiscal deficits may lead to inflationary pressures. It is crucial for

policymakers to maintain a balanced budget and ensure that government spending is directed toward productive sectors

that offer long-term benefits to the economy.

 Strengthening institutional frameworks is crucial for supporting effective policy implementation. The ability of these

countries to return to long-run equilibrium after short-term shocks highlights the importance of strong institutions.

Governments should focus on enhancing the capacity of their monetary and fiscal institutions to respond promptly and

effectively to economic challenges.

 Monetary policy coordination is essential for effective inflation management in the WAMZ countries. Central banks

across the region should collaborate to design inflation-targeting frameworks that account for the varying impacts of

demand for money and interest rates in different countries. By tailoring monetary policies to each nation’s specific

economic conditions, central banks can enhance the effectiveness of their strategies and better manage inflation.

 Strengthening central bank independence is crucial for successfully targeting inflation. Central banks must operate

independently to ensure the consistent application of monetary policy tools, particularly interest rates, without political

interference. This independence will enable central banks to maintain focus on long-term economic stability and

effectively manage inflationary pressures.

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MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue II, February 2025

www.ijltemas.in Page 282

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