 Explaining stationary variables with non-stationary regressorsJohn Baffes () Applied Economics Letters, 1997, vol. 4, issue 1, 69-75 Abstract: When variables included in an OLS regression are stationary, conventional statistical measures such as t-statistics and R2's - in addition to a priori information from economic theory - are the standard indicators used to assess the performance of the hypothesized model. However, if the variables under consideration are non-stationary, such conventional measures no longer have the usual interpretation. With recent developments in time-series analysis, namely cointegration, researchers are able to deal with models containing non-stationary variables effectively. A standard cointegration model, however, requires all variables included in the regression to be of the same order of integration. In this paper we consider a regression in which the dependent variable is integrated of order zero, I(0), while the explanatory variables are integrated of order one, I(1). Conventional statistical measures are inapplicable because the regressors are not stationary. On the other hand, cointegration statistics are inapplicable because the variables are not of the same order of integration. This letter proposes a methodology on how to evaluate the performance of such a model. Date: 1997 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:apeclt:v:4:y:1997:i:1:p:69-75 Ordering information: This journal article can be ordered from DOI: 10.1080/758521836 Access Statistics for this article Applied Economics Letters is currently edited by Anita Phillips More articles in Applied Economics Letters from Taylor & Francis Journals |
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