LAG SELECTION OF THE AUGMENTED KAPETANIOS-SHIN-SNELL NONLINEAR UNIT ROOT TEST

Abstract

We provide simulation evidence that shed light on s everal size and power issues in relation to lag sel ection of the augmented (nonlinear) KSS test. Two lag sele ction approaches are considered-the Modified AIC (MAIC) approach and a sequential General to Specific (GS) testing approach Either one of these approac hes can be used to select the optimal lag based on eith er the augmented linear Dickey Fuller test or the augmented nonlinear KSS test, resulting in four pos sible selection methods, namely, MAIC, GS, NMAIC and NGS. The evidence suggests that the asymptotic critical values of the KSS test tends to result in oversizing if the (N) GS method is used and under-sizin g if the (N) MAIC method is utilised. Thus, we recommend that the critical values should be genera ted from finite samples. We also find evidence that the (N) MAIC method has less size distortion than the ( N) GS method, suggesting that the MAIC-based KSS test is preferred. Interestingly, the MAIC-based KS S test with lag selection based on the linear ADF regression is generally more powerful than the test with lag selection based on the nonlinear version.