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Bootstrap Conditional Distribution Tests In the Presence of Dynamic Misspecification

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  • Valentina Corradi

    ()
    (Queen Mary, University of London)

  • Norman R. Swanson

    ()
    (Rutgers University)

Abstract

In this paper, we show the first order validity of the block bootstrap in the context of Kolmogorov type conditional distribution tests when there is dynamic misspecification and parameter estimation error. Our approach di®ers from the literature to date because we construct a bootstrap statistic that allows for dynamic misspecification under both hypotheses. We consider two test statistics; one is the CK test of Andrews (1997), and the other is in the spirit of Diebold, Gunther and Tay (1998). The limiting distribution of both tests is a Gaussian process with a covariance kernel that reflects dynamic misspecification and parameter estimation error. In order to provide valid asymptotic critical values we suggest an extention of the empirical process version of the block bootstrap to the case of non vanishing parameter estimation error. The findings from Monte Carlo experiments show that both statistics have good finite sample properties for samples as small as 500 observations.

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Bibliographic Info

Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 200311.

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Date of creation: 16 Oct 2003
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Handle: RePEc:rut:rutres:200311

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Keywords: block bootstrap; conditional distributions; conditional Kolmogorov tests; dynamic misspecification; parameter estimation error;

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