Out-of-sample equity premium predictability and sample split--invariant inference
- Published version (with publisher's formatting)
Download (757kB) | Preview
- Final accepted version (with author's formatting)
Restricted to Repository staff and depositor only until 21 April 2018.
Available under License Creative Commons Attribution Non-commercial No Derivatives.
For a comprehensive set of 21 equity premium predictors we find extreme variation in out-of-sample predictability results depending on the choice of the sample split date. To resolve this issue we propose reporting in graphical form the out-of-sample predictability criteria for every possible sample split, and two out-of-sample tests that are invariant to the sample split choice. We provide Monte Carlo evidence that our bootstrap-based inference is valid. The in-sample, and the sample split invariant out-of-sample mean and maximum tests that we propose, are in broad agreement. Finally we demonstrate how one can construct sample split invariant out-of-sample predictability tests that simultaneously control for data mining across many variables.
|Research Areas:||A. > Business School > Economics|
|Notes on copyright:||Access to full text restricted pending copyright check.|
|Depositing User:||Gueorgui Kolev|
|Date Deposited:||19 Oct 2016 09:59|
|Last Modified:||15 Mar 2017 12:40|
Actions (login required)