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Inference in Models with Nearly Integrated Regressors

Published online by Cambridge University Press:  11 February 2009

Christopher L. Cavanagh ,
Graham Elliott  and
James H. Stock
Christopher L. Cavanagh
Affiliation:
Columbia University
Graham Elliott
Affiliation:
University of California, San Diego
James H. Stock
Affiliation:
Kennedy School of Government Harvard University and National Bureau of Economic Research
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Abstract

This paper examines regression tests of whether x forecasts y when the largest autoregressive root of the regressor is unknown. It is shown that previously proposed two-step procedures, with first stages that consistently classify x as I(1) or I(0), exhibit large size distortions when regressors have local-to-unit roots, because of asymptotic dependence on a nuisance parameter that cannot be estimated consistently. Several alternative procedures, based on Bonferroni and Scheffe methods, are therefore proposed and investigated. For many parameter values, the power loss from using these conservative tests is small.

Type
Articles
Information
Econometric Theory , Volume 11 , Issue 5 , October 1995 , pp. 1131 - 1147
Copyright
Copyright © Cambridge University Press 1995

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