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A ridge to homogeneity for linear models
- 1.0532771 - NHU-C 2021 RIV GB eng J - Journal Article
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
R&D Projects: GA ČR(CZ) GA17-26535S; GA ČR(CZ) GA20-28055S
Institutional support: Progres-Q24
Keywords : shrinkage * homogeneity restrictions * ridge regression
OECD category: Applied Economics, Econometrics
Impact factor: 1.424, year: 2020
Method of publishing: Limited access
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.
Permanent Link: http://hdl.handle.net/11104/0311165
Number of the records: 1