Mean-shift least squares model averaging
Econometrics
2019-12-04 v1 Statistics Theory
Methodology
Statistics Theory
Abstract
This paper proposes a new estimator for selecting weights to average over least squares estimates obtained from a set of models. Our proposed estimator builds on the Mallows model average (MMA) estimator of Hansen (2007), but, unlike MMA, simultaneously controls for location bias and regression error through a common constant. We show that our proposed estimator-- the mean-shift Mallows model average (MSA) estimator-- is asymptotically optimal to the original MMA estimator in terms of mean squared error. A simulation study is presented, where we show that our proposed estimator uniformly outperforms the MMA estimator.
Cite
@article{arxiv.1912.01194,
title = {Mean-shift least squares model averaging},
author = {Kenichiro McAlinn and Kosaku Takanashi},
journal= {arXiv preprint arXiv:1912.01194},
year = {2019}
}