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How Reliable are Bootstrap-based Heteroskedasticity Robust Tests? (2005.04089v2)
Published 8 May 2020 in math.ST, econ.EM, stat.ME, and stat.TH
Abstract: We develop theoretical finite-sample results concerning the size of wild bootstrap-based heteroskedasticity robust tests in linear regression models. In particular, these results provide an efficient diagnostic check, which can be used to weed out tests that are unreliable for a given testing problem in the sense that they overreject substantially. This allows us to assess the reliability of a large variety of wild bootstrap-based tests in an extensive numerical study.