Predictive regressions for macroeconomic data

TL;DR

This paper introduces new empirical likelihood methods to test stock return predictability using macroeconomic data, effectively handling nonstationarity and heavy tails in the predictors.

Contribution

It develops novel weighted score-based empirical likelihood techniques that are robust to nonstationary and heavy-tailed macroeconomic predictors.

Findings

Methods perform well in theory and practice.

Effective for nonstationary and heavy-tailed data.

Applicable to predicting stock returns from macroeconomic ratios.

Abstract

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.