Inference after Model Averaging in Linear Regression Models

32 Pages Posted: 20 Sep 2017

See all articles by Xinyu Zhang

Xinyu Zhang

Chinese Academy of Sciences (CAS) - Academy of Mathematics and Systems Sciences

Chu-An Liu

Academia Sinica - Institute of Economics

Date Written: April 17, 2017

Abstract

This paper considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estimator (MMA; Hansen, 2007) and the jackknife model averaging estimator (JMA; Hansen and Racine, 2012) under the standard asymptotics with fixed parameters setup. We find that both MMA and JMA estimators asymptotically assign zero weight to the under-fitted models, and MMA and JMA weights of just-fitted and over-fitted models are asymptotically random. Building on the asymptotic behavior of model weights, we derive the asymptotic distributions of MMA and JMA estimators and propose a simulation-based confidence interval for the least squares averaging estimator. Monte Carlo simulations show that the coverage probabilities of proposed confidence intervals achieve the nominal level.

Keywords: Confidence Intervals, Inference Post-Model-Averaging, Jackknife Model Averaging, Mallows Model Averaging

JEL Classification: C51, C52

Suggested Citation

Zhang, Xinyu and Liu, Chu-An, Inference after Model Averaging in Linear Regression Models (April 17, 2017). Available at SSRN: https://ssrn.com/abstract=3039829 or http://dx.doi.org/10.2139/ssrn.3039829

Xinyu Zhang

Chinese Academy of Sciences (CAS) - Academy of Mathematics and Systems Sciences ( email )

Zhong-Guan-Cun-Dong-Lu 55, Haidian District
Beijing, 100190, P.R., Beijing 100190
China

Chu-An Liu (Contact Author)

Academia Sinica - Institute of Economics ( email )

128 Academia Road, Section 2
Nankang
Taipei, 11529
Taiwan

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