Residual-augmented IVX predictive regression

ABSTRACT

Bias correction in predictive regressions is known to reduce the empirical size problems of OLS-based predictability tests with persistent predictors. This paper shows that bias correction is also achieved in the context of the extended instrumental variable (IVX) predictability testing framework introduced by Kostakis et al. (2015). To be specific, new IVX-based statistics subject to a bias correction analogous to that proposed by Amihud and Hurvich (2004) are introduced. Four important contributions are provided: first, we characterize the effects that bias-reduction adjustments have on the asymptotic distributions of the IVX test statistics in a general context allowing for short-run dynamics and heterogeneity; second, we discuss the validity of the procedure when predictors are stationary as well as near-integrated; third, we conduct an exhaustive Monte Carlo analysis to investigate the small in- and out-of-sample properties of the test procedures and their sensitivity to distinctive features that characterize predictive regressions in practice, such as strong persistence, endogeneity, and non-Gaussian innovations; and fourth, we provide an analysis of real estate return and rent growth predictability in 19 OECD countries.

WITH

Matei Demetrescu, Department of Statistics, TU Dortmund University, Germany

link to this paper

About Author(s)

Paulo Rodrigues

Nova Faculty Affiliate

Paulo M. M. Rodrigues obtained his PhD in Econometrics from the School of Economic Studies of the University of Manchester. He is a professor of economics at Nova School of Business and Economics, a senior research economist at the Bank of Portugal, and a fellow of both the Clive Granger Centre for Time Series Analysis of the University of Nottingham and the University of Essex´s Centre for Financial Econometrics.