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Heteroskedasticity-and-Autocorrelation-Consistent Bootstrapping

Russell Davidson and Andrea Monticini

No def012, DISCE - Working Papers del Dipartimento di Economia e Finanza from Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE)

Abstract: In many, if not most, econometric applications, it is impossible to estimate consistently the elements of the white-noise process or processes that underlie the DGP. A common example is a regression model with heteroskedastic and/or autocorrelated disturbances,where the heteroskedasticity and autocorrelation are of unknown form. A particular version of the wild bootstrap can be shown to work very well with many models, both univariate and multivariate, in the presence of heteroskedasticity. Nothing comparable appears to exist for handling serial correlation. Recently, there has been proposed something called the dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and merits considerable further study.

Keywords: Bootstrap; time series; wild bootstrap; dependent wild bootstrap; HAC covariance matrix estimator (search for similar items in EconPapers)
JEL-codes: C12 C22 C32 (search for similar items in EconPapers)
Pages: 18
Date: 2014-03
New Economics Papers: this item is included in nep-ecm and nep-ore
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