Optimal HAR inference

Optimal HAR inference

This paper addresses the problem of deriving heteroskedasticity and autocorrelation robust (HAR) inference for a scalar parameter of interest, under the assumption of a known upper bound on data persistence. Finite-sample optimal tests are derived within the Gaussian location model, revealing that r...

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Bibliographic Details
Main Author: DOU, Liyu
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
Subjects:
heteroskedasticity
autocorrelation
robust inference
maximal persistence
equal-weighted cosine test
Gaussian location model
long-run variance
Student-t adjustment
statistical efficiency
empirical examples
Econometrics
Statistics and Probability
Online Access:https://ink.library.smu.edu.sg/soe_research/2787
https://ink.library.smu.edu.sg/context/soe_research/article/3786/viewcontent/harinf.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:This paper addresses the problem of deriving heteroskedasticity and autocorrelation robust (HAR) inference for a scalar parameter of interest, under the assumption of a known upper bound on data persistence. Finite-sample optimal tests are derived within the Gaussian location model, revealing that robustness-efficiency tradeoffs are primarily determined by the maximal persistence. With a suitable adjustment to the critical value, the equal-weighted cosine (EWC) test emerges as nearly optimal, wherein the long-run variance is estimated through projections onto q type II cosines. This approach establishes a direct link between the choice of q and persistence assumptions, accompanied by adjustments to the conventional Student-t critical value. The findings are demonstrated through two empirical examples.

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