Robust Nonnested Testing for Ordinary Least Squares Regression When Some of the Regressors are Lagged Dependent Variables

Leslie G. Godrey

Discussion Papers from Department of Economics, University of York

Abstract: The problem of testing nonnested regression models that include lagged values of the dependent variable as regressors is discussed. It is argued that it is essential to test for error autocorrelation if ordinary least squares and the associated J and F tests are to be used. A heteroskedasticity-robust joint test against a combination of the artificial alternatives used for autocorrelation and nonnested hypothesis tests is proposed. Monte Carlo results indicate that implementing this joint test using a wild bootstrap method leads to a well-behaved procedure and gives better control of finite sample significance levels than asymptotic critical values.

Keywords: nonnested models; heteroskedasticity-robust; wild bootstrap (search for similar items in EconPapers)
JEL-codes: C12 C15 C52 (search for similar items in EconPapers)
Date: 2010-10
New Economics Papers: this item is included in nep-ecm
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