Semiparametric Single-Index Predictive Regression
60 Pages Posted: 4 Aug 2018 Last revised: 4 Sep 2019
Date Written: July 15, 2018
This paper studies a semi-parametric single-index predictive regression model with multiple nonstationary predictors that exhibit co-movement behaviour. Orthogonal series expansion is employed to approximate the unknown link function in the model and the estimator is derived from an optimization under the constraint of identification condition for index parameter. The main finding includes two types of super-consistency rates of the estimators of the index parameter along two orthogonal directions in a new coordinate system. The central limit theorem is established for a plug-in estimator of the unknown link function. In the empirical studies, we provide evidence in favour of nonlinear predictability of the stock return using long term yield and treasury bill rate.
Keywords: Predictive Regression; Single-Index Model; Hermite Orthogonal Estimation; Dual Super-Consistency Rates; Co-Moving Predictors
JEL Classification: C22; C53; G12
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