Publication Type
Journal Article
Version
submittedVersion
Publication Date
9-2015
Abstract
In this paper we consider model averaging for quantile regressions (QR) when all models under investigation are potentially misspecified and the number of parameters is diverging with the sample size. To allow for the dependence between the error terms and regressors in the QR models, we propose a jackknife model averaging (JMA) estimator which selects the weights by minimizing a leave-one-out cross-validation criterion function and demonstrate its asymptotic optimality in terms of minimizing the out-of-sample final prediction error. We conduct simulations to demonstrate the finite-sample performance of our estimator and compare it with other model selection and averaging methods. We apply our JMA method to forecast quantiles of excess stock returns and wages. (C) 2015 Elsevier B.V. All rights reserved.
Keywords
Final prediction error, High dimensionality, Model averaging, Model selection, Misspecification, Quantile regression
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
188
Issue
1
First Page
40
Last Page
58
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2014.11.005
Publisher
Elsevier
Citation
LU, Xun and SU, Liangjun.
Jackknife model averaging for quantile regressions. (2015). Journal of Econometrics. 188, (1), 40-58.
Available at: https://ink.library.smu.edu.sg/soe_research/1873
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2014.11.005