Cointegration and Granger Causality Analysis

Although selection of the number of lags is not necessary when conducting ARDL, a lag of 3 was selected following SBIC, AIC and HQIC criteria. Results of the ARDL (3, 3, 3, 3, 3, 3) are presented in Table 9.3. The Wald statistic is used to examine the integration relationship. However, because this statistic does not have a standard distribution, critical values provided in Pesaran et al. (2001) are used for the test. If the computed F statistic is lower than the critical lower bound, the null hypothesis is not rejected and the conclusion is that there is no long run relationship between credits provided to the private sector and the independent variables, and vice versa where the F statistic is higher than the upper bound. The result is inconclusive if the obtained F statistic falls in between the lower and the upper bound. The F statistic is obtained when each variable is considered as a dependent variable. The results indicated presence of 2 cointegrating relationships in a model. This rank was chosen, using a trace statistic of 37.98, which was less than the critical value of 47.21. A bounds test was conducted to investigate the relationships further. The bounds test results are presented in Table 9.3.

Table 9.3 Bounds Test Results

Dependent variable AIC lags F statistic* Decision

PS credit by banks 3 8.83 Cointegration

Bank deposits 3 8.19 Cointegration

Broad money 3 2.78 No cointegration

Inflation rate 3 3.06 No cointegration

Credit to government 3 2.79 No cointegration

Gross domestic product 3 1.28 No cointegration

* Lower bound critical value at 1% 4.32 and upper bound critical value at 1% 5.624 (Adapted from Pesaran et al., 2001)

The results of the bounds test, in Table 9.3, indicate that there is a long run relationship among the variables in the model when private sector credit by banks and bank deposits are dependent variables. However, for inflation rate, broad money, GDP and credit to

government variables the null hypothesis of no cointegration fails to be rejected. This result confirms the earlier cointegrating rank test results, which indicated presence of two cointegrating relationships among the variables. Although, cointegration indicates presence of Granger causality (in one direction), the direction of causality among the variables is better detected through use of a vector error correction model (VECM) (Odhiambo, 2009). Table 9.4 to Table 9.6 displays results for Granger causality for private sector credit by banks variable.

Table 9.4 Long Run Autoregressive Distributed Lag Estimates

Variable Coefficient

(standard error) P value

Bank deposits -0.42 (0.02) 0.0000***

Broad money 0.27 (0.03) 0.0000***

Gross domestic product -0.19 (0.1) 0.0000*** Credit to government 0.21 (0.02) 0.0000***

ECM(-1) -0.48 (0.13) 0.0000***

***Significance at 1%

The fit for the ARDL underlying equation was good and significant overall at 1% level. The diagnostic tests of Durbin Watson, Breusch-Godfrey, Engle’s Lagrange Multiplier (ARCH) and Jarque-Bera indicated a good model fit (see Table 9.5). The negative coefficient of the error correction model in Table 9.4 indicates significant long run causality from the independent variables to private sector credit by banks. Bank deposits, broad money, credit to government and GDP growth rate have a significant effect on private sector credit by banks (DCPSB) in the long run. A negative relationship is observed for bank deposit and GDP. A possible explanation is that not all deposits mobilised by the bank are invested in private sector credit (see Chapter 4.1.) Credit to government and statutory bodies are positively related to DCPSB. Growth in gross domestic product is not only contributed by credit extended by banks, and hence the negative relationship. Grants, credit from other development institutions, and foreign direct investments are among other significant factors. This result supports the findings of Simwaka et al. (2012) who reported that for the Malawi economy, capacity utilisation rather than financial development has a significant impact on economic growth.

In the short run only bank deposits in the second lag and broad money has a positive and significant effect on DCPSB (see Table 9.5). Pairwise Granger causality results for all the

variables are presented in Table 9.6. There is a one-way causality from DCPSB to GDP, supporting the hypothesis that financial development can cause economic growth through the credit channel. However, for the case of Malawi, the absence of causality running from bank deposits to DCPSB could indicate that banks invest most of the mobilised deposits in money markets and other high earning, less risky financial assets at the expense of credit to private sector. However a positive association observed in the short run could indicate that bank mobilised deposits are invested in short term credit to the private sector. This is in response to the unfavourable macroeconomic environment. High interest, high inflation rates and fluctuating value of currency, contributed to a net domestic credit to the government between 2000 and 2004, which affected real investments in Malawi (Gondwe, 2005). The null hypothesis of no Granger causality from bank deposits to private sector credit by banks is not rejected.

Table 9.5 Short Run Estimates for Private Sector Credit by Banks

Variable Coefficient P value

BD(-2) 1.20 0.001***

BM (-1) 0.64 0.093*

***significance at 1% *significance at 10%

Adjusted R2 = 0.77, F statistic = 8.83, p<0.01, standard error = 0.24 Breusch-Pagan chi2 0.81, p>0.05

LM test (Lag1)32.69, p= 0.62; (Lag2) 45.73, p=0.12 Durbin Watson 2.51

Jarque-Bera test χ2( 5.753) , p = 0.93

Table 9.6 Granger Causality for Private Sector Credit by Banks

Null hypothesis F Statistic P value

Credit by bank does not Granger cause GDP 24.76 0.0000***

GDP does not Granger cause credit by bank 5.70 0.0577**

Bank deposits does not Granger cause GDP 9.95 0.0001***

Broad money does not Granger cause Bank deposits 52.10 0.0000***

Broad money does not Granger cause GDP 15.58 0.0000***

Credit to government does not Granger cause broad money 8.44 0.0147** Credit by banks does not Granger cause credit to government 9.85 0.0073*** Credit to government does not Granger cause GDP 11.81 0.0027*** ***significance at 1%, **significance at 5%, *significance at 10%