Mallows criterion for heteroskedastic linear regressions with many regressors

摘要

We present a feasible generalized Mallows criterion for model selection for a linear regression setup with conditional heteroskedasticity and possibly numerous explanatory variables. The feasible version exploits unbiased individual variance estimates from recent literature. The property of asymptotic optimality of the feasible criterion is shown. A simulation experiment shows large discrepancies between model selection outcomes and those yielded by the classical Mallows criterion or other available alternatives. (C) 2021 Elsevier B.V. All rights reserved.