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

Xiaoqing Ye () and Yixiao Sun
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Xiaoqing Ye: South-Central University for Nationalities

Stata Journal, 2018, vol. 18, issue 4, 951-980

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 ap- proximations 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 hypoth- esis 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 com- mands, 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.

Keywords: har; hart; gmmhar; gmmhart; heteroskedasticity- and auto- correlation-robust inference; fixed-smoothing; kernel function; orthonormal series; testing-optimal; AMSE; OLS/IV; two-step GMM; J statistic (search for similar items in EconPapers)
Date: 2018
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Working Paper: Heteroscedasticity and Autocorrelation Robust F and t Tests in Stata (2018)
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