Testing many restrictions under heteroskedasticity

0574156 - NHU-C 2024 RIV NL eng J - Journal Article
Anatolyev, Stanislav - Sølvsten, M.
Testing many restrictions under heteroskedasticity.
Journal of Econometrics. Roč. 236, č. 1 (2023), č. článku 105473. ISSN 0304-4076. E-ISSN 1872-6895
R&D Projects: GA ČR(CZ) GA20-28055S
Institutional support: Cooperatio-COOP
Keywords : linear regression * ordinary least squares * many regressors
OECD category: Applied Economics, Econometrics
Impact factor: 6.3, year: 2022
Method of publishing: Limited access
https://doi.org/10.1016/j.jeconom.2023.03.011

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.
Permanent Link: https://hdl.handle.net/11104/0344497