The Money Demand Model (Equation 1)
In the demand for money model, it can be seen from the above results that the coefficient of determination, also known as the "goodness of fit" is relatively very high. This shows that 87%
of the changes in the demand for real money balances can be accounted for by the explanatory variables used in this model. This is supported by a high F-statistic of 22.08, which shows that the coefficients. in this regression are jointly significant. The high F-statistic with its associated low probability rejects the null hypothesis that all the coefficients of the explanatory variables except that of the constant/intercept term are zero and accept the alternative that there is indeed a relationship.
In addition, the residuals from the regression on the demand for real money balances were also tested for stationarity. The higher absolute ADF value of -4.24 than the Mackinnon critical value
of -3.64 suggèsts that there is cointegration between money demand and the explanatory variables in the madel. This again validates the regression and one can safely conclude that spurious correlation is absent in this equation.
It should be understood that the above models based on the levels are the long-run relationships
as opposed to the short-run relationships, which are also presented in the next section. Since this long-run relationship has also been validated i.e. cointegration has been found as shawn above, we can now go ahead to interpret the coefficients in the madel. The above models are significant in that they demonstrate the long-run economie relations upon which most economie theories are based.
On the other hand, the dynamic models give us the short-run relations as differencing leads to loss of information in the long-run. However a combination of the two sets of models and the interpretations therefrom would help give an insight into the determinants of money demand and
domestic savings rate in The Gambia and the adjustment mechanism from the long- to the short-term. A careful examination of the two situations would highlight the policy interventions possible and to what extent as can be understood from the coefficients of the explanatory variables in the two scenarios.
As regards the scale variable in the money demand function, which in this case is real GNP (RGNPMl), the expected positive a priori sign has been validated by the study. The results show
that there is a positive relationship between income and the demand for money and the variable is significant at the 1% level. Our finding is also similar to that of Kariuki in Kenya. The coefficient of 1 .. 58, which in this model represents an elasticity since the variables of the model are in logarithmic form, shows that a 1% change in income can lead to a 1.58% change in the demand for real money balances in The Gambia. In other words, the demand for real money balances in The Gambia over the period of the study is income elastic. This is also similar to results obtained by Khan and Ali in Pakistan who found the income elasticity of demand for real money balances in the neighbourhood of 1.1.
The real interest rate (RDPR) has a negative impact on the demand for real money balances in conformity with Keynesian theoretical expectations. However, the positive relationship postulated by the financialliberalization hypothesis (also established by Oshikoya and Kariuki in Kenya) cannot be validated by Gambian data over the period studied. The real interest rate is statistically significant at the 1% level.
As regards the investment rate (IRATE), the impact is negative contrary to theoretical expectations. However, the negative and significant relationship obtained is intuitively appealing since if investment increases, it would be reasonable for the demand to hold money by individuals and households in the form of cash and other liquid assets to also fall.
The Durbin Watson statistics of2.007 which is almost exactly 2 and the Breusch-Godfrey seriai correlation test (see table 4.4, page 66) show that there is no evidence of first or higher-order autocorrelation in the equation errors, while the other tests to be presented also support the view
The dummy variable representing the overall structural adjustment programme adopted in 1985 in The Gambia (a policy shift dummy) shows that the overall programme has a negative impact on the demand for money and this variable is also significant at the 1% level.
We therefore conclude that for the full sample, the model adequately captures the salient features of the data and is consistent with the main implications of economie theory.
The Domestic Savings Function (Equation 2)
In equation 2, we estimated the domestic savings model for The Gambia. Similarly, the coefficient of determination of 64 % is a good fit. It shows that 64% of the changes in domestic savings in The Gambia can be accounted for by the explanatory variables used. The F-statistic of 5.81 (greater th~ 2) shows that the variables are jointly significant in explaining the variations in domestic savings in The Gambia.
The residual from the regression was tested for stationarity in order to test for the presence of cointegration. From the results, it can be seen that the residual is stationary thus suggesting cointegration. Since the lag of the residual has ADF value of -3.14 which is greater in absolute terms than the Mackinnon critical value of -3.08, there is a cointegrating relationship between savings and its determinants. This was further confirmed in the dynamic equations in the next section by a procedure to be explained later.
The dependency ratio has a negative impact on domestic savings mobilization in The Gambia in conformity with theoretical expectations. The results suggest very strongly that a 1% change in the dependency ratio can lead to 18.89% opposite change in domestic savings; in other words a
63
1% increase in the dependency ratio can lead to a fall in domestic savings mobilization of 18.89% and vice versa. The variable is significant at the 1% level. As defined already, age dependency ratio refers to the summation of 65+ and 0-14 age groups as a proportion of the economically a~tive population (15-64). The study and the results thus obtained suggest that the higher the economically inactive population in relation to the economically active, the greater the difficulty of mobilizing domestic savings for investment and this is also intuitive! y appealing.
The real deposit interest rate has a positive impact on savings mobilization and the variable is significant at the 5% level. This finding which is similar to that of Oshikoya in Kenya also lends sorne support to the financial liberalization theory that high real interest rates can increase savings. It should be added that the correlation by and of itself does not imply causation but economie theory suggests that the causation rnight be from interest rates to domestic savings.
The GDP growth rate variable has a positive impact on savings and in fact the variable has a strong impact, which is significant at the 1% level. This is in Iine with the ECA's Economie Report for Africa, which states among other things that "The most important determinant of savings in Africa has been found to be the leve! of real income" (ECA, 1999 pp.xiii-xiv). The above results suggest that Gambia is by no means an exception to this.
The significant impact of foreign savings found by sorne researchers (positive or negative), could not be established statistically with our methodology, scope of analysis and country of illustration. The foreign savings rate has a negative impact on domestic savings suggesting substitution between domestic and foreign savings but no statistical significance could be
attached to this correlation and the variable in question was subsequently dropped from the model.
The coefficient of the po licy shift dummy representing the overall adjustment programme, which cannot be quantified, has a positive impact on savings and is statistically significant at the 5%
level. As a result of this, one may conclude that the other measures such as privatization of public enterprises, expenditure controls among others that helped to reduce budget deficits might also have a positive impact on domestic savings mobilization in The Gambia.
4.4 Diagnostic Tests
Diagnostic tests were also conducted on the above equations to determine the degree to which they violate or validate the basic assumptions of the Ordinary Least Squares. Violation of these assumptions would affect the reliability of inferences drawn from them and non-violation would further validate the models estimated.
The Breusch-Godfrey Seriai Correlation LM test was conducted in a bid to diagnose the presence of seri al correlation in the residuals. This test is to be preferred to the D-W statistic as a measure of seriai correlation for the following main reasons: (i) the Durbin-Watson test is not an appropriate measure of autocorrelation if among the explanatory variables there are lagged values of the endogenous variable and; (ii) the Durbin-Watson test is inappropriate for testing for higher ordet seriai correlation or for other forms of autocorrelation (e.g. non-linear forms of the seriai dependence of the residual values). As a result of these drawbacks that limit its application, alternative tests such as the Breusch-Godfrey SC LM test have been suggested by various writers including Durbin himself. Renee this test was preferred for the study. The test
confinns the findings from the Durbin-Watson statistic that there is no autocorrelation as a statistical problem in any of the above models. The Jarque-Bera test for nonnality also shows that the error terms in all the estimated models are normally distributed with mean zero and have constant variances.
The models also pass the ARCH LM test for heteroscedasticity. This finding was further supported by the White heteroscedasticity test that was conducted both with and without cross terms. The Ramsey regression specification test (RESET) and the Chow forecast test demonstrate that there was stability in both models. The Ramsey further shows that the models are well specified. From the above findings, we may conclude that the models have passed all the diagnostic tests conducted· A detailed explanation of the diagnostic tests for the money demand and domestic savings functions for The Gambia over the scope of the study are presented in technical annex 1. In general, desirable outcomes of these tests are low F -statistics with high probability values as shown below:
Table 4.4. Diagnostics Tests Results for Equation 1
Type of Test
Table 4.4a. Diagnostics Tests Results for Equation 2
Type of Test F -Statistics Probability
Breusch-Godfrey (SCLM) 0.95 0.42
Jarque-Bera (Normality) 0.08* 0.96
ARCH (LM) 0.19 0.82
White Heteroschedasticity 1.2 0.47
Ramsey RESET 2.5 0.13
Chow Forecast ** 1.02 0.40
*the Jarque-Bera Statistic
** forecast is from 1995 to 1997
The Real Interest Rate and the ERP
ln order to investigate the impact of the overall structural adjustment programme on the real interest rate variable and hence on domestic savings mobilization, the dummy representing the overall programme was removed from the domestic savings equation and the two results compared. Here we examined the interest rate coefficient, which significantly dropped from 0.035 to a very low value of 0.014. In fact it was demonstrated that without the dummy, the interest rate variable was not significant in explaining domestic savings mobilisation in The Garnbia whereas with the dummy it is significant at the 5% level. From this we can conclude that the ERP whic~ implemented the fmancial liberalisation policy indeed matters for domestic savings mobilisation at least as far as the real interest rate variable is concemed. The results of domestic savings equation minus dummy are presented in table 4.2b in page 68 for comparison.
Table 4.2b: Domestic Savings Equations with and without Dummy
Domestic Savings Domestic Savings
Function(without dummy) Function(with dummy)
Variable Coefficient Variable Coefficient
c
41.28 (1.85)c
86.09 (-3.07)Log(DPNR) -8.78 (-1.79) Log(DPNR) -18.89 (-3.015)
RDPR 0.014 (1.49) RDPR 0.035 (2.79)
GRATE 0.057 (3.20) GRATE 0.061 (4.37)
AR(1) 0.80 (3.21) DMMY 1.36 (2.14)
AR(2) -0.29 (-1.15) AR(1) 0.86 (3.77)
AR(2) -0.57 ( -2.57)
R"· 0.72 F-stat.(5.65) R.t- 0.77 F-Stat.(5.81)
R2(Adjusted)=0.59
Prob (F) 0.008) R2(Adj.) = 0.64 Prob (F) = 0.008