1 Problem Statement
3.2 Empirical Analysis
As a first step, we specify four equations ( la, lb, le and 2) in order to determine which best explains the case for Sierra Leone. Equation (2) is the same as specified above while the other equations are specified as follows:
LMt
=f(LPb LY b DUM)
(la)LMt = f(LPb LY b DUM, LMt-1)
(lb)LM
t =f(LP b L Yb DUM, LMt-1 ,LEt) (le)
Equation (1 a) is identical to equation (1) but accounts for the civil war by incorporating the dummy variable. It typically follows the traditional import demand specification. Equation (1 b)
ex tends equation (1 a) by incorporating the lagged import variable to further account for adjustment cost. Equation (le) extends equation (la) by adding export earnings to account for foreign exchange constraints. We then estimate these four using OLS for the period 1966-96 and the results are shawn in table 3.
From table 3, we see that the estimated coefficients of ail the variables in equation (1 a) have the correct signs and are statisticaily significant at the 5% level of significance. The R2 is 0.73 percent and the DW is 1.19. With the inclusion of the lagged import variable, the goodness of fit increases from 73 percent to 76 percent. When export earnings is introduce into the equation, the goodness of fit jumped from 76 to 93 percent. This shows that the availability of foreign exchange is very significant in the determination of import demand in Sierra Leone.
Finaily, we estimated equation (2) by introducing money supply into equation (le). This resulted in a further increase of the goodness of fit from 93 to 95 percent. Thus, we concluded that in terms of goodness of fit, equation (2) best explains import demand in Sierra Leone in comparison to the other estimated equations. The coefficient of almost ail the variables we also found to be significant and in conformity with economie theory.
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Table 3: Regression Results
EQ(1a) Coefficient EQ(1b) Coefficient EQ(1c) Coefficient EQ(2) Coefficient
LP
-0.18( -4.52) LP -0.11( -2.15) LP -0.12( -4.01) LP -0.12(-4.74)LY
0.73(4.68) LY 0.37(1.64) LY 0.28(2.19) LY -0.11( -0.57)DM -0.67(-2.76) DM -0.45(-1.73) DM -0.31( -2.06) DM -0.25( -2.00) LM(-1) 0.39(1.79) LM(-1) 0.21(1.63) LM(-1) 0.29(2.63)
LX 0.54(7.33) LX 0.67(7.07)
LMS 0.20(3.03)
Rz 0.76 Rz 0.78 Rz 0.93 Rz 0.95
Adj. Rl 0.73 Adj. R2 0.74 Adj. R2 0.91 Adj. R2 0.93
DW 1.19 DW 1.62 DW 1.92 DW 2.02
F-Stats. 28.89 F-Stats. 22.5 F-Stats. 66.98 F-Stats. 75.86
Prob.F 0.00 Prob.F 0.00 Prob.F 0.00 Prob.F 0.00
Note: t-statistics are in parenthesis
The numerical magnitude of the coefficient of the priee variable leads us to safely conclude that the priee elasticity do not only have the expected negative sign but is also significant at the 1%
level. This is in conformity with economie theory that bas long postulated that priees and demand are inversely related. This implies that there is a large response of imports to changes in relative priees. In other words, devaluation can be an effective tool influencing import demand in Sierra Leone.
Sorne studies [see for example Kabir (1988)] have found that for developing countries, priees are insignificant in determining imports in developing countries even though they do have a negative relationship. Our findings, though contrary to sorne studies are in line with studies done by Khan (1974) and Reinhart (1995). Although Khan did not consider the stationary properties of the data used in his study, Reinhart accounted for stationary by employing co-integration technique.
Both Reinhart and Khan concluded that, for a host of developing countries the Marshall-Lerner condition for successful devaluation holds implying that priees are significant in determining import demand in developing countries. Our results thus show that when country specifie characteristics are accounted for the regression results tends to vary from a general results for group of countries.
The estimated income elasticity for Sierra Leone does not only have a negative sign but is also insignificant even at the 10% level of confidence. According to Kabir (1988) the sign of income elasticity need not be positive. This ambiguity is because imports represent the difference between consumption of imported goods and the production of importables. Therefore, as real income increase, consumption of imported goods could rise faster or slower than the production so that imports could rise or fall. This is not surprising in the case of Sierra Leone because food imports accounts for 39 percent of total imports between 1990-9611 and rice the staple food accounted for 49 percent of food imports within the same period. If rice production increases, the consumption of imported rice will fall especially given the fact that the preference is for the locally produced variety. Also, the a priori expectation of a positive relationship between imports and income stems from the Keynesian expression which assumes that the majority of
11 Bank of Siena Leone Bulletin 1996
imported goods are intermediate products used in the production process. As this is clearly not the case for Sierra Leone, the negative relationship between income and import should be no cause for alarm.
We used export eamings as a measure of foreign exchange availability. From the works of Mckinnon (1964), Chenery and Strout (1966) and Piekarz and Skeckler (1967), we expected a positive relationship between import demand and export eamings. Our result conforms to this expectation, as the coefficient is positive and significant at the 1% leve] of significance.
We had also incorporated money supply in our function to determine it effects on import demand given the lack of developed financial market in Sierra Leone. A priori expectation dictates a positive relationship between money supply and import demand. Our result supports this expectation as the coefficient of money supply variable was found to be positive and significant at the 1 o/olevel.
The estimated coefficient of the dummy variable we also found to be significant at the 5% leve]
with a negative sign. This goes to confirrn that the civil war has a negative impact on import demand in Sierra Leone. Thus our results underscores the need to end the conflict in Sierra Leone.
We had accounted for adjustment time by incorporating import lagged one period based on Khan's (1974) specification. The inclusion of this variable increase the goodness of fit and its
coefficient is significant at the 1% level. This shows that import adjust over time and that habit persistence influences import demand in Sierra Leone.
In general, our results are good and supports our specification by conforming mostly to trade theories. The goodness of fit, measured by the R2 is 0.95 implying that 95% of the changes in import demand can be explained by the variables specified in our import demand function. The corresponding F-statistics of 75.86 with a low probability of 0.00 is a clear indication that the variables are jointly significant. The Durbin-Watson statistic of 2.02 indicates that auto-correlation is absent in our specification. However, to further validate the results of our regression, we proceeded to conduct diagnostic tests. The results for these test are presented in table 4.
Table 4 Diagnostic Tests for Equation (2)
TEST F-STATISTICS PROBABILITY
Jarque-Bera 2.57 0.27
Bruesch-Godfrey 0.02 0.97
ARCH 0.07 0.78
WHITE 0.94 0.53
RAMSEY 0.16 0.85
CHOW 0.43 0.83
The Jarcque-Bera test is used to determine the normality of the distribution of the residuals in the regression. If this distribution is not normal, a basic assumption of the OLS estimation method bas been violated. The Breusch-Godfrey test is used to detect the presence of auto-correlation in the model. lt is superior to DW test especially when a lagged dependent variable is introduced as an explanatory variable. Ours being the case, the value of the Breusch-Godfrey test is significant in determining whether the import demand function is free of this problem.
The ARCH and WHITE tests helps to detect homoscedasticity in the disturbance term. The null hypothesis underlying these tests is that the disturbance terms are homoscedastic. Therefore the presence of heteroscedasticity violates an OLS basic assumption. The WHITE test is more robust as it tests for heteroscedasticity as weil as mis-specification.
The RAMSEY test is used specifically to detect mis-specification of the regression model.
These mis-specifications usually occur due to omission of relevant explanatory variables or incorrect specification of the functional form. RAMSEY test therefore regresses the dependent variable on explanatory variables and fitted values of the dependent variables with increasing function of powers. The Chow test measures the constancy of the parameter estimates and thus the stability of the estimated equation over the forecast periods.
For ali the diagnostics tests, a low value with a corresponding probability value greater than 5 % is an indication of good result. Therefore from table 4, we conclude that our import demand function [equation (2)] passes ali the diagnostic tests.