Publication Type

Journal Article

Publication Date

4-2015

Abstract

A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The limit distribution of these predictive tests is nuisance parameter free and holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit root processes. Asymptotic theory and simulations show that the proposed tests are more powerful than existing parametric predictability tests when deviations from unity are large or the predictive regression is nonlinear. Empirical illustrations to monthly SP500 stock returns data are provided. (C) 2014 Elsevier B.V. All rights reserved.

Keywords

Fractional Ornstein-Uhlenbeck process, Functional regression, Nonparametric predictability test, Nonparametric regression, Stock returns, Predictive regression

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

185

Issue

2

First Page

468

Last Page

494

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2014.05.015

Publisher

Elsevier

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.2014.05.015

Included in

Econometrics Commons

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