David I. Harvey
Stephen J. Leybourne
Robert Sollis
A. M. Robert Taylor

real‐time detection of regimes of predictability in the us equity premium (replication data)

We propose new real-time monitoring procedures for the emergence of end-of-sample predictive regimes using sequential implementations of standard (heteroskedasticity-robust) regression t-statistics for predictability applied over relatively short time periods. The procedures we develop can also be used for detecting historical regimes of temporary predictability. Our proposed methods are robust to both the degree of persistence and endogeneity of the regressors in the predictive regression and to certain forms of heteroskedasticity in the shocks. We discuss how the monitoring procedures can be designed such that their false positive rate can be set by the practitioner at the start of the monitoring period using detection rules based on information obtained from the data in a training period. We use these new monitoring procedures to investigate the presence of regime changes in the predictability of the US equity premium at the 1?month horizon by traditional macroeconomic and financial variables, and by binary technical analysis indicators. Our results suggest that the 1-month-ahead equity premium has temporarily been predictable, displaying so-called pockets of predictability, and that these episodes of predictability could have been detected in real time by practitioners using our proposed methodology.

Data and Resources

Suggested Citation

Harvey, David I.; Leybourne, Stephen J.; Sollis, Robert; Taylor, A. M. Robert (2021): Real‐time detection of regimes of predictability in the US equity premium (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022327.0715863367