Simula@BI seminar: Jack Fosten, King's Business School
Simula@BI invites senior lecturer Jack Fosten, King’s Business School, King’s College London, to talk about Predictive Ability Tests with Possibly Overlapping Models (econometrics).
Predictive Ability Tests with Possibly Overlapping Models (with V. Corradi and D. Gutknecht)
This paper provides novel tests for comparing out-of-sample predictive ability of two or more competing models that are possibly overlapping. The tests do not require pre-testing, they allow for dynamic misspecification and are valid under different estimation schemes and loss functions.
In pairwise model comparisons, the test is constructed by adding a random perturbation to both the numerator and denominator of a standard Diebold-Mariano test statistic. This prevents degeneracy in the presence of overlapping models but becomes asymptotically negligible otherwise. The test has correct size uniformly over all null data generating processes.
A similar idea is used to develop a superior predictive ability test for the comparison of multiple models against a benchmark. Monte Carlo simulations demonstrate that our tests exhibit very good size control in finite samples reducing both incidences of under- and over-sizing relative to its competitors. Finally, an application to forecasting U.S. excess bond returns provides evidence in favour of models using macroeconomic factors.