Open Access
March 2014 Predictive regressions for macroeconomic data
Fukang Zhu, Zongwu Cai, Liang Peng
Ann. Appl. Stat. 8(1): 577-594 (March 2014). DOI: 10.1214/13-AOAS708


Researchers have constantly asked whether stock returns can be predicted by some macroeconomic data. However, it is known that macroeconomic data may exhibit nonstationarity and/or heavy tails, which complicates existing testing procedures for predictability. In this paper we propose novel empirical likelihood methods based on some weighted score equations to test whether the monthly CRSP value-weighted index can be predicted by the log dividend-price ratio or the log earnings-price ratio. The new methods work well both theoretically and empirically regardless of the predicting variables being stationary or nonstationary or having an infinite variance.


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Fukang Zhu. Zongwu Cai. Liang Peng. "Predictive regressions for macroeconomic data." Ann. Appl. Stat. 8 (1) 577 - 594, March 2014.


Published: March 2014
First available in Project Euclid: 8 April 2014

zbMATH: 06302248
MathSciNet: MR3192003
Digital Object Identifier: 10.1214/13-AOAS708

Keywords: autoregressive process , empirical likelihood , long memory process , nearly integrated , predictive regressions , unit root , weighted estimation

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.8 • No. 1 • March 2014
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