Testing many restrictions under heteroskedasticity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F23%3A00582611" target="_blank" >RIV/67985998:_____/23:00582611 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11640/23:00574156

Výsledek na webu

<a href="https://doi.org/10.1016/j.jeconom.2023.03.011" target="_blank" >https://doi.org/10.1016/j.jeconom.2023.03.011</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jeconom.2023.03.011" target="_blank" >10.1016/j.jeconom.2023.03.011</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Testing many restrictions under heteroskedasticity

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Testing many restrictions under heteroskedasticity

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/GA20-28055S" target="_blank" >GA20-28055S: EKONOMETRIE S PŘEPARAMETRIZOVANÝMI MODELY A SLABOU IDENTIFIKACÍ</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Econometrics

ISSN

0304-4076

e-ISSN

1872-6895

Svazek periodika

236

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

19

Strana od-do

105473

Kód UT WoS článku

001032935200001

EID výsledku v databázi Scopus

2-s2.0-85163996332