Bootstrap HAC Tests for Ordinary Least Squares Regression

Authors


  • We are grateful to two anonymous referees, Peter Burridge, Russell Davidson, Sílvia Gonçalves, Joao Santos Silva and Peter N. Smith for their helpful comments and suggestions.

Abstract

There is a need for tests that are derived from the ordinary least squares (OLS) estimators of regression coefficients and are useful in the presence of unspecified forms of heteroskedasticity and autocorrelation. A method that uses the moving block bootstrap and quasi-estimators in order to derive a consistent estimator of the asymptotic covariance matrix for the OLS estimators and robust significance tests is proposed. The method is shown to be asymptotically valid and Monte Carlo evidence indicates that it is capable of providing good control of significance levels in finite samples and good power compared with two other bootstrap tests.

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