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.
Highlights
Citation count that is close to that of the most heavily cited Journal of Econometrics paper of the past five
The Modified Akaike Information Criterion (AIC) (MAIC)-based KSS 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
We report the results of Monte Carlo simulations designed to investigate the size and power performance of the KSS test incorporated with the four different augmentation lag selection strategies, namely, MAIC, nonlinear MAIC (NMAIC), General to Specific (GS), N-GS
Summary
Citation count that is close to that of the most heavily cited Journal of Econometrics paper of the past five. Kapetanios et al (2003) propose to non-linear but stationary time series that are a unit-root test using an auxiliary regression model that appropriate to characterize some economic and/or approximates the Exponential Smooth Transition financial time series (Michael et al, 1997; Taylor, 2001; Autoregressive (ESTAR) process by Taylor (2001). Innovations is, as suggested by Said and Dickey (1984), Kapetanios et al (2003) is probably the most widely approximated by an augmented autoregression with a recognized and applied (According to Hanck (2012) and truncated lag k. An important issue is the choice of the Kapetanios et al (2003) “as evidenced by e.g., a Scopus truncated lag (k) which has vital size and power
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.