Publication Type

Journal Article

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

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1016/j.jeconom.2014.11.005

Included in

Econometrics Commons

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