Nonparametric predictive regression

被引：26

作者：

Kasparis, Ioannis ^{[1
]}

Andreou, Elena ^{[1
,2
]}

Phillips, Peter C. B. ^{[3
,4
,5
,6
]}

机构：

[1] Univ Cyprus, CY-1678 Nicosia, Cyprus

[2] CERP, Toronto, ON, Canada

[3] Yale Univ, New Haven, CT 06520 USA

[4] Univ Auckland, Auckland 1, New Zealand

[5] Univ Southampton, Southampton SO9 5NH, Hants, England

[6] Singapore Management Univ, Singapore 178902, Singapore

来源：

JOURNAL OF ECONOMETRICS | 2015年 / 185卷 / 02期

基金：

欧洲研究理事会; 美国国家科学基金会;

关键词：

Fractional Ornstein-Uhlenbeck process; Functional regression; Nonparametric predictability test; Nonparametric regression; Stock returns; Predictive regression; ASYMPTOTIC THEORY; STOCK RETURNS; COINTEGRATION; INFERENCE; MISSPECIFICATION; PREDICTABILITY; APPROXIMATION; CONVERGENCE; FUNCTIONALS; INTEGRATION;

D O I：

10.1016/j.jeconom.2014.05.015

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

引用

页码：468 / 494

页数：27

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