Nonparametric predictive regression

Ioannis Kasparis, Elena Andreou and Peter Phillips

Journal of Econometrics, 2015, vol. 185, issue 2, 468-494

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.

Keywords: Fractional Ornstein–Uhlenbeck process; Functional regression; Nonparametric predictability test; Nonparametric regression; Stock returns; Predictive regression (search for similar items in EconPapers)
JEL-codes: C22 C32 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

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Related works:
Working Paper: Nonparametric Predictive Regression (2013)
Working Paper: Nonparametric Predictive Regression (2012)
Working Paper: Nonparametric Predictive Regression (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:185:y:2015:i:2:p:468-494

DOI: 10.1016/j.jeconom.2014.05.015

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