Least Squares Model Averaging for Two Non-Nested Linear Models

Least Squares Model Averaging for Two Non-Nested Linear Models

This paper studies the least squares model averaging methods for two non-nested linear models. It is proved that the Mallows model averaging weight of the true model is root- n consistent. Then the authors develop a penalized Mallows criterion which ensures that the weight of the true model equals 1...

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Veröffentlicht in:Journal of systems science and complexity 2023-02, Vol.36 (1), p.412-432
Hauptverfasser: Gao, Yan, Xie, Tianfa, Zou, Guohua
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Sprache:eng
Schlagworte:
Asymptotic properties
Complex Systems
Control
Least squares
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Statistics
Systems Theory
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Zou, Guohua
description This paper studies the least squares model averaging methods for two non-nested linear models. It is proved that the Mallows model averaging weight of the true model is root- n consistent. Then the authors develop a penalized Mallows criterion which ensures that the weight of the true model equals 1 with probability tending to 1 and thus the averaging estimator is asymptotically normal. If neither candidate model is true, the penalized Mallows averaging estimator is asymptotically optimal. Simulation results show the selection consistency of the penalized Mallows method and the superiority of the model averaging approach compared with the model selection estimation.
doi_str_mv 10.1007/s11424-023-1172-6
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subjects Asymptotic properties
Complex Systems
Control
Least squares
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Statistics
Systems Theory
title Least Squares Model Averaging for Two Non-Nested Linear Models
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