Inference after Model Averaging in Linear Regression Models
32 Pages Posted: 20 Sep 2017
Date Written: April 17, 2017
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: Suggested Citation