Higher order properties of the wild bootstrap under misspecification
AbstractWe examine the higher order properties of the wild bootstrap (Wu, 1986) in a linear regression model with stochastic regressors. We find that the ability of the wild bootstrap to provide a higher order refinement is contingent upon whether the errors are mean independent of the regressors or merely uncorrelated with them. In the latter case, the wild bootstrap may fail to match some of the terms in an Edgeworth expansion of the full sample test statistic. Nonetheless, we show that the wild bootstrap still has a lower maximal asymptotic risk as an estimator of the true distribution than a normal approximation, in shrinking neighborhoods of properly specified models. To assess the practical implications of this result we conduct a Monte Carlo study contrasting the performance of the wild bootstrap with a normal approximation and the traditional nonparametric bootstrap.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 171 (2012)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/locate/jeconom
Wild bootstrap; Misspecification; Edgeworth expansion;
Other versions of this item:
- Patrick M. Kline & Andres Santos, 2011. "Higher Order Properties of the Wild Bootstrap Under Misspecification," NBER Working Papers 16793, National Bureau of Economic Research, Inc.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
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