Testing many restrictions under heteroskedasticity

Stanislav Anatolyev and Mikkel Sølvsten

Journal of Econometrics, 2023, vol. 236, issue 1

Abstract: We propose a hypothesis test that allows for many tested restrictions in a heteroskedastic linear regression model. The test compares the conventional F statistic to a critical value that corrects for many restrictions and conditional heteroskedasticity. This correction uses leave-one-out estimation to correctly center the critical value and leave-three-out estimation to appropriately scale it. The large sample properties of the test are established in an asymptotic framework where the number of tested restrictions may be fixed or may grow with the sample size, and can even be proportional to the number of observations. We show that the test is asymptotically valid and has non-trivial asymptotic power against the same local alternatives as the exact F test when the latter is valid. Simulations corroborate these theoretical findings and suggest excellent size control in moderately small samples, even under strong heteroskedasticity.

Keywords: Linear regression; Ordinary least squares; Many regressors; Leave-out estimation; Hypothesis testing; High-dimensional models (search for similar items in EconPapers)
JEL-codes: C12 C13 C21 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Working Paper: Testing Many Restrictions Under Heteroskedasticity (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:236:y:2023:i:1:s0304407623001677

DOI: 10.1016/j.jeconom.2023.03.011

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