Cost & opportunities in misspecified predictive regressions

Theoretical contributions

• Comparisons of methods: tests between integral formulae and Monte-Carlo simulations. Requires the R functions and C functions.
• Sensitivity analysis: impact of persistence, forecasting horizon and sample size on loss / R².
• Univariate learning: comparison between PRs and univariate learning, that is, regression on lagged values of the process.

Empirical results

All empirical results are based on this RMarkdown notebook and this function file. The data (.RData format) is based on that of Welch and Goyal (2008) plus one variable from Novy-Marx (2014). Sources of the analyses:

• Impact of rho_y
• Value of economic constraints
• Aggregate variance forecasting

References

• Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21(4), 1455-1508.
• Novy-Marx, R. (2014). Predicting anomaly performance with politics, the weather, global warming, sunspots, and the stars. Journal of Financial Economics, 112(2), 137-146.