Ridging out many covariates

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11640%2F25%3A00641355" target="_blank" >RIV/00216208:11640/25:00641355 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985998:_____/25:00646397

Výsledek na webu

<a href="https://doi.org/10.1080/03610926.2025.2490691" target="_blank" >https://doi.org/10.1080/03610926.2025.2490691</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/03610926.2025.2490691" target="_blank" >10.1080/03610926.2025.2490691</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ridging out many covariates

Popis výsledku v původním jazyce

The article considers a conditionally heteroskedastic linear regression setup with few regressors of interest and many nuisance covariates. We propose to subject the parameters corresponding to those nuisance covariates to a generalized ridge shrinkage. We show that under the assumption of dense random effects from the nuisance covariates, the ridge-out estimator of the parameters of interest is conditionally unbiased, and we derive the optimal ridge intensity that delivers conditional efficiency. When tight structures on the variance of random effects are imposed, the asymptotic variance of the ridge-out estimator, under the dimension asymptotics, may be arbitrarily smaller than that of the least squares estimator. We also demonstrate how the optimal ridge-out estimator can be implemented under tight structures on the variance of random effects and run simulation experiments where significant efficiency gains are possible to reach.

Název v anglickém jazyce

Ridging out many covariates

Popis výsledku anglicky

The article considers a conditionally heteroskedastic linear regression setup with few regressors of interest and many nuisance covariates. We propose to subject the parameters corresponding to those nuisance covariates to a generalized ridge shrinkage. We show that under the assumption of dense random effects from the nuisance covariates, the ridge-out estimator of the parameters of interest is conditionally unbiased, and we derive the optimal ridge intensity that delivers conditional efficiency. When tight structures on the variance of random effects are imposed, the asymptotic variance of the ridge-out estimator, under the dimension asymptotics, may be arbitrarily smaller than that of the least squares estimator. We also demonstrate how the optimal ridge-out estimator can be implemented under tight structures on the variance of random effects and run simulation experiments where significant efficiency gains are possible to reach.

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/GA24-12720S" target="_blank" >GA24-12720S: EKONOMETRICKÉ METODY ROBUSTNÍ VŮČI DIMENZIONALITĚ PARAMETRŮ A SHLUKOVÁNÍ DAT</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Communications in Statistics - Theory and Methods

ISSN

0361-0926

e-ISSN

1532-415X

Svazek periodika

54

Číslo periodika v rámci svazku

24

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

8064-8078

Kód UT WoS článku

001482703400001

EID výsledku v databázi Scopus

2-s2.0-105004432280