This page contains research and presentation material on the topic of
quadratic errors in predictive regressions.

This is joint work with
**Romain
Deguest**.

- The
**theoretical contributions**of the paper are decomposed into:**Comparisons of methods**: tests between integral formulae and Monte-Carlo simulations. This requires**R functions**and**C functions**.**Sensitivity analysis**: impact of persistence, forecasting horizon and sample size on loss / R^2.

**Univariate learning**: comparison between PRs and univariate learning, that is, regression on lagged values of the process.

- All our
**empirical results**are based on the following**RMarkdown notebook**and the function**file**. The data can be found**here**(.RData format). It is based on that of Welch and Goyal (2008) plus one variable from Novy-Marx (2014).

The source for the multiple analyses are the following:**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.

**DISCLAIMER**: the **data** and
**code** are meant for research and pedagogical use
only.