Ridging out many covariates
The result's identifiers
Result code in 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>
Alternative codes found
RIV/67985998:_____/25:00646397
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Ridging out many covariates
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
Project
<a href="/en/project/GA24-12720S" target="_blank" >GA24-12720S: ECONOMETRIC METHODS ROBUST TO PARAMETER DIMENSION AND DATA CLUSTERING</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Communications in Statistics - Theory and Methods
ISSN
0361-0926
e-ISSN
1532-415X
Volume of the periodical
54
Issue of the periodical within the volume
24
Country of publishing house
GB - UNITED KINGDOM
Number of pages
15
Pages from-to
8064-8078
UT code for WoS article
001482703400001
EID of the result in the Scopus database
2-s2.0-105004432280