A ridge to homogeneity for linear models

0540210 - NHÚ 2021 RIV GB eng J - Článek v odborném periodiku
Anatolyev, Stanislav
A ridge to homogeneity for linear models.
Journal of Statistical Computation and Simulation. Roč. 90, č. 13 (2020), s. 2455-2472. ISSN 0094-9655. E-ISSN 1563-5163
Institucionální podpora: RVO:67985998
Klíčová slova: shrinkage * homogeneity restrictions * ridge regression
Obor OECD: Applied Economics, Econometrics
Impakt faktor: 1.424, rok: 2020
Způsob publikování: Omezený přístup
https://doi.org/10.1080/00949655.2020.1779722

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.
Trvalý link: http://hdl.handle.net/11104/0317851