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The Stata Journal
Volume 18 Number 4: pp. 951-980



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Heteroskedasticity- and autocorrelation-robust F and t tests in Stata

Xiaoqing Ye
School of Mathematics and Statistics
South-Central University for Nationalities
Wuhan, China
yshtim@126.com
Yixiao Sun
Department of Economics
University of California, San Diego
La Jolla, CA
yisun@ucsd.edu
Abstract.  In this article, we consider time-series, ordinary least-squares, and instrumental-variable regressions and introduce a new pair of commands, har and hart, that implement more accurate heteroskedasticity- and autocorrelation-robust (HAR) F and t tests. These tests represent part of the recent progress on HAR inference. The F and t tests are based on the convenient F and t approximations and are more accurate than the conventional chi-squared and normal approximations. The underlying smoothing parameters are selected to target the type I and type II errors, which are the two fundamental objects in every hypothesis testing problem. The estimation command har and the postestimation test command hart allow for both kernel HAR variance estimators and orthonormal-series HAR variance estimators. In addition, we introduce another pair of new commands, gmmhar and gmmhart, that implement the recently developed F and t tests in a two-step generalized method of moments framework. For these commands, we opt for the orthonormal-series HAR variance estimator based on the Fourier bases because it allows us to develop convenient F and t approximations as in the first-step generalized method of moments framework. Finally, we present several examples to demonstrate these commands.
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View all articles by these authors: Xiaoqing Ye, Yixiao Sun

View all articles with these keywords: har, hart, gmmhar, gmmhart, heteroskedasticity- and au-to-cor-re-la-tion-robust inference, fixed-smoothing, kernel function, orthonormal series, testing-optimal, AMSE, OLS/IV, two-step GMM, J statistic

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