Bootstrap HAC tests for ordinary least squares regressions

Francesco Bravo; Leslie George Godfrey

doi:10.1111/j.1468-0084.2011.00671.x

Bootstrap HAC tests for ordinary least squares regressions

Francesco Bravo, Leslie George Godfrey

Economics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	903-922
Number of pages	20
Journal	Oxford Bulletin of Economics and Statistics
Volume	74
Issue number	6
Early online date	24 Oct 2011
DOIs	https://doi.org/10.1111/j.1468-0084.2011.00671.x
Publication status	Published - Dec 2012

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10.1111/j.1468-0084.2011.00671.x

Cite this

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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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AB - 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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DO - 10.1111/j.1468-0084.2011.00671.x

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