Mallows criterion for heteroskedastic linear regressions with many regressors

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11640%2F21%3A00544276" target="_blank" >RIV/00216208:11640/21:00544276 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.econlet.2021.109864" target="_blank" >https://doi.org/10.1016/j.econlet.2021.109864</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.econlet.2021.109864" target="_blank" >10.1016/j.econlet.2021.109864</a>

Result language
angličtina
Original language name
Mallows criterion for heteroskedastic linear regressions with many regressors
Original language description
We present a feasible generalized Mallows criterion for model selection for a linear regression setup with conditional heteroskedasticity and possibly numerous explanatory variables. The feasible version exploits unbiased individual variance estimates from recent literature. The property of asymptotic optimality of the feasible criterion is shown. A simulation experiment shows large discrepancies between model selection outcomes and those yielded by the classical Mallows criterion or other available alternatives.
Czech name
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Czech description
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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

Project
<a href="/en/project/GA20-28055S" target="_blank" >GA20-28055S: ECONOMETRICS WITH OVERPARAMETERIZATION AND WEAK IDENTIFICATION</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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