*More Credits, Less Cash: A Panel Cointegration Approach DOI: http://dx.doi.org/10.5772/intechopen.93778*


**Table 4.**

evidence to reject the null hypothesis at 1% significance level for variables. It means that second-generation unit root tests are more appropriate in order to decide whether variables are stationary or not. However, test result for the residuals obtained from error correction model fails to reject the null hypothesis at any significance level. This result provides support for presence of cross-sectional inde-

cointegration tests should be used. Westerlund [16] was chosen to explore long-run

There is not enough evidence to reject the null hypothesis of homogeneity tests at any significance level with respect to results presented in **Table 2**. The results indicate strong evidence for homogeneity of slope coefficients. Therefore, Westerlund [16] is suitable to explore cointegration relation if variables are nonstationary. Pesaran [15] CIPS unit root test was used in order to examine stationarity of variables. **Table 3** reports results of the CIPS unit root test for level

The test results in **Table 3** fail to reject the null hypothesis of CIPS unit root test in level of all variables. This result gives evidence of non-stationarity of variables. It means that a shock in the economy has permanent effect on liquidity risk and credit expansion. However, the results provide support for stationarity of variables after differencing them. Liquidity risk and credit expansion are integrated of order 1 (I (1)). Due to integration level of variables, panel cointegration relation can be analyzed. Selection of appropriate panel cointegration method depends on crosssectional dependence and homogeneity of residuals. Westerlund [16] cointegration test was chosen due to homogeneity and cross-sectional independence of residuals. Westerlund's [16] null hypothesis indicates that there is not long-term relation between variables. Four statistics were calculated in Westerlund [16]. Test results

**Δ**~ 0.417 0.676 **Δ**~ *adj* 0.590 0.555

**Variables Deterministic term Pesaran CIPS statistic [15]**

*\*\*\*Indicates that the results can reject the null hypothesis at 1% significance level. The relevant 1% critical value for the*

LR Intercept only 2.198 **<sup>Δ</sup>LR** Intercept only 4.421\*\*\* CE Intercept only 1.780 **<sup>Δ</sup>CE** Intercept only 3.991\*\*\*

*cross-sectionally augmented Dickey-Fuller (CADF) statistic suggested by Pesaran is 2.1 [15].*

*Note: Deterministic term was chosen by exploring graphs by panel.*

**Statistic p-Value**

pendence in the error correction model. In this case, first-generation panel

dynamics. However, Homogeneity tests should be realized before applying Westerlund [16]. If panel is homogenous then Westerlund's [16] results are valid. For this purpose, Pesaran and Yamagata [22] homogeneity test was applied to error

correction model. Test results are given in **Table 2**.

*Linear and Non-Linear Financial Econometrics - Theory and Practice*

and first difference of variables.

were given in **Table 4**.

*Test results of homogeneity tests.*

*Δ represents first differences of variables.*

*Test results of CIPS unit root test.*

**Table 2.**

**Table 3.**

**212**

*Test results of Westerlund [16] cointegration test.*


#### **Table 5.**

*Estimation results of long-run relation model.*

Westerlund [16] cointegration test results show rejection of the null hypothesis for all statistics. It points out that there is a long-term relationship between liquidity risk and credit expansion. Since the variables are cointegrated, long-run relationship can be estimated. Eq. (18) was estimated by the PDOLS estimation method developed by Kao and Chiang [42] in order to investigate the effect of credit expansion on liquidity risk in the long run. The estimation results were given in **Table 5**.

The Wald statistics in **Table 5** is significant at 1% level. It means that model is generally significant. The estimated parameter is the long-term parameter and it is statistically significant at 1% level. Therefore, the credit expansion affects the liquidity risk in the long run. This means that 1% increase in credit expansion increases liquidity risk by 1.31%.
