Citation:
Anatolyev, Stanislav (2020) "A ridge to homogeneity for linear models", Journal of Statistical Computation and Simulation, vol. 90, no. 13, pp. 2455-2472
Abstract:
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.
Link to paper online:
Journal of Statistical Computation and Simulation, vol. 90, no. 13, pp. 2455-2472
Paper in accepted version:
Presented at:
Workshop in Model Selection, Regularization, and Inference in Vienna, 2018
12th International Conference on Computational and Financial Econometrics, Pisa, 2018
5th Conference of Deutsche Arbeitsgemeinschaft Statistik, Munich, 2019
Czech Economic Society and Slovak Economic Association Meeting, Brno, 2019