A ridge to homogeneity for linear models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F20%3A00540210" target="_blank" >RIV/67985998:_____/20:00540210 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11640/20:00532771
Result on the web
<a href="https://doi.org/10.1080/00949655.2020.1779722" target="_blank" >https://doi.org/10.1080/00949655.2020.1779722</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/00949655.2020.1779722" target="_blank" >10.1080/00949655.2020.1779722</a>

Result language
angličtina
Original language name
A ridge to homogeneity for linear models
Original language description
In some heavily parameterized models, one may benefit from shifting some of parameters towards a common target. We consider L2 shrinkage towards an equal parameter value that balances between unrestricted estimation (i.e. allowing full heterogeneity) and estimation under equality restriction (i.e. imposing full homogeneity). The penalty parameter of such ridge regression estimator is tuned using leave-one-out cross-validation. The reduction in predictive mean squared error tends to increase with the dimensionality of the parameter set. We illustrate the benefit of such shrinkage with a few stylized examples. We also work out an example of a heterogeneous panel model, including estimation on real data.
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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