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Abstract

This paper derives the limiting distributions of least squares averaging estimators for linear regression models in a local asymptotic framework. We show that the averaging estimators with fixed weights are asymptotically normal and then develop a plug-in averaging estimator that minimizes the sample analog of the asymptotic mean squared error. We investigate the focused information criterion Claeskens and Hjort, 2003, the plug-in averaging estimator, the Mallows model averaging estimator Hansen, 2007, and the jackknife model averaging estimator Hansen and Racine, 2012. We find that the asymptotic distributions of averaging estimators with data-dependent weights are nonstandard and cannot be approximated by simulation. Toaddress this issue, we propose a simple procedure to construct valid confidence intervals with improved coverage probability. Monte Carlo simulations show that the plug-in averaging estimator generally has smaller expected squared error than other existing model averaging methods, and the coverage probability of proposed confidence intervals achieves the nominal level. As an empirical illustration, the proposed methodology is applied to cross-country growth regressions.



Item Type: MPRA Paper -

Original Title: Distribution Theory of the Least Squares Averaging Estimator-

Language: English-

Keywords: Local asymptotic theory, Model averaging, Model selection, Plug-in estimators.-

Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and EstimationC - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection-





Autor: Liu, Chu-An

Fuente: https://mpra.ub.uni-muenchen.de/54201/







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