Bootstrapping The Autoregressivedistributed Lag Test For Cointegration - Sam, Chung Yan (2016)
The objective of this thesis is to examine the performances of a cointegration test: Autoregressive Distributed Lag (ARDL) bounds test approach developed by Pesaran et al. (2001). This approach gained popularity and is widely used for over two decades due to its advantages of super consistent estimation and dealing with mixed integration order regressors. Nevertheless, the ARDL bounds test is often applied in environments that are inconsistent with the assumptions underlying that framework. This approach assumes that there is no feedback at level from the dependent variable to the regressors. That is, all variables except one must be weakly exogenous. Estimation involving several plausibly endogenous variables as regressors will give biased and misleading results. However, through simulation evidence our results show that the performance of the bounds test approach is not affected by this endogeneity problem. In this thesis, we propose a new ARDL cointegration test that relies on the bootstrap procedure. It is shown that by introducing a proper bootstrap procedures, some weaknesses underlying the approach are improved based on size and power properties. In addition, it eliminates the possibility of inconclusive inferences from bounds testing. Besides that, inference based solely on the significance of F test and single t test is insufficient to avoid degenerate case. Conducting an additional testing on the lagged independent variable comes from the proposed method to provide a better insight in concluding the status of cointegration. The empirical relevance of the bootstrap ARDL test is demonstrated by an estimation of savinginvestment correlations.