Bootstrap Conditional Distribution Tests In the Presence of Dynamic Misspecification
Valentina Corradi and
Norman Swanson ()
Departmental Working Papers from Rutgers University, Department of Economics
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.
Keywords: block bootstrap; conditional distributions; conditional Kolmogorov tests; dynamic misspecification; parameter estimation error (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Bootstrap conditional distribution tests in the presence of dynamic misspecification (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:200311
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