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

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  • Leslie G. Godrey
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    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.

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    Paper provided by Department of Economics, University of York in its series Discussion Papers with number 10/22.

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    Date of creation: Oct 2010
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    Handle: RePEc:yor:yorken:10/22

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    Keywords: nonnested models; heteroskedasticity-robust; wild bootstrap;

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    1. GONÇALVES, Silvia & KILIAN, Lutz, 2003. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques 2003-01, Universite de Montreal, Departement de sciences economiques.
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