Autoregressive Distributed Lag Models and Cointegration

Abstract

This paper considers cointegration analysis within an autoregressive distributed lag (ADL) framework. First, different reparameterizations and interpretations are reviewed. Then we show that the estimation of a cointegrating vector from an ADL specification is equivalent to that from an error-correction (EC) model. Therefore, asymptotic normality available in the ADL model under exogene-ity carries over to the EC estimator. Next, we review cointegration tests based on EC regressions. Special attention is paid to the effect of linear time trends in case of regressions without detrending. Finally, the relevance of our asymptotic results in finite samples is investigated by means of computer experiments. In particular, it turns out that the conditional EC model is superior to the unconditional one.KeywordsCointegration TestError Correction ModelCointegration AnalysisStochastic TrendLinear Time TrendThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.