Title: A ridge to homogeneity
Authors: Stanislav Anatolyev - CERGE-EI and New Economic School (Czech Republic) [presenting]
Abstract: In some heavily parameterized econometric models, one may benefit from shrinking a subset of parameters towards a common target. We consider $L_2$ 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 one-leave-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, both theoretically and empirically, a heterogenous linear panel data setup and compare several estimators and corresponding confidence intervals.