**5. Empirical results**

The aim of this study is to examine the long-term relationship between liquidity risk and credit expansion for the period from 2014.Q1 to 2017.Q4 using data from 20 banks in the Turkish banking sector. Since biased results can be obtained due to correlation between units forming panel data, the presence of cross-sectional dependence should be tested first. In this context, the presence of cross-sectional dependence of residuals obtained from error correction model and cross-sectional dependence of the liquidity risk and credit expansion variables were tested by Pesaran [14] CD test. The test results are given in **Table 1**.

According to the results represented in **Table 1**, the null hypothesis of crosssectional dependence test states no correlation between units. There is enough


**Table 1.** *Test results of cross-sectional dependence.*

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 independence in the error correction model. In this case, first-generation panel cointegration tests should be used. Westerlund [16] was chosen to explore long-run 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**.

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 and first difference of variables.

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 were given in **Table 4**.

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

**Test statistic Test value z-Value p-Value GT** 2.943 5.785 0.000 **G<sup>α</sup>** 14.235 5.804 0.000 **PT** 10.502 3.857 0.000 **P<sup>α</sup>** 7.343 2.912 0.002

**LR Coefficient z-Value**

(0.133)

9.82\*\*\*

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

Banks convert short-term assets received from depositors to long-term debt for borrowers. Therefore, banks try to maximize their expected profits by considering the risks that may arise from their activities. The concept of risk here is the state of uncertainty, which is uncertain but effective on institutional goals. Liquidity risk is one of the important risks faced by banks. Therefore, many studies on liquidity risk have been conducted in the literature. However, while assets and liabilities are two important components that constitute a bank's balance sheet, a panel study investigating long-run relation between credit expansion and liquidity risk has not been conducted in Turkey. This study aims to fill this gap in the literature. Panel cointegration approach was adopted in order to explore long-run dynamics. First, two important factors in panel methodology which are cross-sectional dependence and homogeneity were investigated properly. Pesaran [14] CD test was applied to the variables and error correction model in order to decide whether there is a crosssectional dependence between units. The null hypothesis of Pesaran [14] CD test which states that there is a dependence between units was rejected for the variables,

**Table 5**.

**Table 5.**

**Table 4.**

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

*Note: Standard error is given in brackets. \*\*\*Indicates significant at 1% level.*

*Estimation results of long-run relation model.*

CE 1.31

*More Credits, Less Cash: A Panel Cointegration Approach*

*DOI: http://dx.doi.org/10.5772/intechopen.93778*

**Wald χ<sup>2</sup>**(1) 96.50\*\*\*

**6. Conclusion**

**213**

increases liquidity risk by 1.31%.


#### **Table 2.**

*Test results of homogeneity tests.*


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

*\*\*\*Indicates that the results can reject the null hypothesis at 1% significance level. The relevant 1% critical value for the cross-sectionally augmented Dickey-Fuller (CADF) statistic suggested by Pesaran is 2.1 [15]. Δ represents first differences of variables.*

#### **Table 3.**

*Test results of CIPS unit root test.*
