05 Août There were periods when banks allocated their loans to several sectors with high-profit expectations but containing many risks
The paper applies the generalized method of moments (GMM) estimator to estimate our dynamic panel model. This estimator is robust to possible multicollinearity, heteroskedasticity, and autocorrelation issues (Greene, 2012 ). Notably, the endogeneity problem (caused by the reciprocal relationship between explanatory and dependent variables or the omission of variables) could be well tackled by the GMM dynamic estimator. There are two different versions of the dynamic panel GMM estimator: (i) the difference GMM estimator performs regressions on the converted equation in the first-difference form, using variables in levels as instruments (Arellano Bond, 1991 ), and (ii) the system GMM estimator conducts regressions on both primitive and transformed equations (level and first-difference equations), employing variables in levels and differences as instruments (Arellano Bover, 1995 ; Blundell Bond, 1998 ). This study prefers the system GMM estimator since it has been improved on the basis of the difference GMM version to yield better estimates (Roodman, 2009 ). The two-step estimator is also selected because it is more efficient than the one-step version, especially for the system GMM estimator (Windmeijer, 2005 ). To avoid the problem of too many instruments in the GMM estimator that could destroy the power of regressions, we limit the proliferation of instruments using the procedure proposed by Roodman ( 2009 ), based on the rule that the number of created instruments should not exceed the number of banks. The usage of the dynamic system GMM estimator is justified by mandatory diagnostic tests, namely, the Hansen test and the Arellano-Bond tests. The null hypothesis of the Hansen test is that the instruments employed are not correlated with the residuals, meaning that the set of instruments is valid.