Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/320317 
Authors: 
Year of Publication: 
2024
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 15 [Issue:] 4 [Year:] 2024 [Pages:] 1107-1149
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
This paper considers the problem of deriving heteroskedasticity and autocorrelation robust (HAR) inference about a scalar parameter of interest. The main assumption is that there is a known upper bound on the degree of persistence in data. I derive finite-sample optimal tests in the Gaussian location model and show that the robustness-efficiency tradeoffs embedded in the optimal tests are essentially determined by the maximal persistence. I find that with an appropriate adjustment to the critical value, it is nearly optimal to use the so-called equal-weighted cosine (EWC) test, where the long-run variance is estimated by projections onto q type II cosines. The practical implications are an explicit link between the choice of q and assumptions on the underlying persistence, as well as a corresponding adjustment to the usual Student-t critical value. I illustrate the results in two empirical examples.
Subjects: 
Heteroskedasticity and autocorrelation robust inference
long-runvariance
JEL: 
C12
C18
C22
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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