Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-26T08:23:19.093Z Has data issue: false hasContentIssue false

TESTS OF NONNESTED HYPOTHESES IN NONSTATIONARY REGRESSIONS WITH AN APPLICATION TO MODELING INDUSTRIAL PRODUCTION

Published online by Cambridge University Press:  01 March 2000

John C. Chao
Affiliation:
University of Maryland
Norman R. Swanson
Affiliation:
Texas A&M University

Abstract

In the context of I(1) time series, we provide some asymptotic results for the Davidson-MacKinnon J-type test. We examine both the case where our regressor sets x1t and x2t are not cointegrated, and the case where they are. In the former case, the OLS estimator of the weighting coefficient from the artificial compound model converges at rate T to a mixed normal distribution, and the associated t-statistic has an asymptotic standard normal distribution. In the latter case, we find that the J-test also has power against violation of weak exogeneity (with respect to the short-run coefficients of the null model), which is caused by correlation between the disturbance of the null model and that of the cointegrating equation linking x1t and x2t. Moreover, unlike the previous case, the OLS estimator of the weighting coefficient from the artificial compound model converges at \sqrt{T} to an asymptotic normal distribution when the null model is specified correctly. In an empirical illustration, we use the tests to examine an industrial production data set for six countries.

Type
Research Article
Copyright
© 2000 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)