%0 Journal Article
%@ 0735-0015
%A Lazarus, Eben
%A Lewis, Daniel J
%A Stock, James H
%A Watson, Mark W
%D 2018
%F discovery:10160486
%I Informa UK Limited
%J Journal of Business & Economic Statistics
%K Heteroscedasticity- and autocorrelation-robust estimation, HAC, Long-run variance.
%N 4
%P 541-559
%T HAR Inference: Recommendations for Practice
%U https://discovery.ucl.ac.uk/id/eprint/10160486/
%V 36
%X The classic papers by Newey and West (1987) and Andrews (1991) spurred a large body of work on how to improve heteroscedasticity- and autocorrelation-robust (HAR) inference in time series regression. This literature finds that using a larger-than-usual truncation parameter to estimate the long-run variance, combined with Kiefer-Vogelsang (2002, 2005) fixed-b critical values, can substantially reduce size distortions, at only a modest cost in (size-adjusted) power. Empirical practice, however, has not kept up. This article therefore draws on the post-Newey West/Andrews literature to make concrete recommendations for HAR inference. We derive truncation parameter rules that choose a point on the size-power tradeoff to minimize a loss function. If Newey-West tests are used, we recommend the truncation parameter rule S = 1.3T 1/2 and (nonstandard) fixed-b critical values. For tests of a single restriction, we find advantages to using the equal-weighted cosine (EWC) test, where the long run variance is estimated by projections onto Type II cosines, using ν = 0.4T 2/3 cosine terms; for this test, fixed-b critical values are, conveniently, tν or F. We assess these rules using first an ARMA/GARCH Monte Carlo design, then a dynamic factor model design estimated using a 207 quarterly U.S. macroeconomic time series.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.