Finite Sample Properties of Tests Based on Prewhitened Nonparametric Covariance Estimators
Abstract
We analytically investigate size and power properties of a popular family of procedures for testing linear restrictions on the coefficient vector in a linear regression model with temporally dependent errors. The tests considered are autocorrelation-corrected F-type tests based on prewhitened nonparametric covariance estimators that possibly incorporate a data-dependent bandwidth parameter, e.g., estimators as considered in Andrews and Monahan (1992), Newey and West (1994), or Rho and Shao (2013). For design matrices that are generic in a measure theoretic sense we prove that these tests either suffer from extreme size distortions or from strong power deficiencies. Despite this negative result we demonstrate that a simple adjustment procedure based on artificial regressors can often resolve this problem.
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Cite
@article{arxiv.1409.1419,
title = {Finite Sample Properties of Tests Based on Prewhitened Nonparametric Covariance Estimators},
author = {David Preinerstorfer},
journal= {arXiv preprint arXiv:1409.1419},
year = {2015}
}
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