Solving the Forecast Combination Puzzle

Abstract

We demonstrate that the so-called forecasting combination puzzle is a consequence of the methodology commonly used to produce forecast combinations. By the combination puzzle, we refer to the empirical finding that predictions formed by combining multiple forecasts in ways that seek to optimize forecast performance often do not out-perform more naive, e.g. equally-weighted, approaches. In particular, we demonstrate that, due to the manner in which such forecasts are typically produced, tests that aim to discriminate between the predictive accuracy of such competing combinations can have low power, and can lack size control, leading to an outcome that favors the simpler approach. In short, we show that this counter-intuitive result can be completely avoided by the adoption of more efficient estimation strategies in the production of the combinations, when feasible. We illustrate these findings both in the context of forecasting a functional of interest and in terms of predictive densities.

(Joint with David T. Frazier, Ryan Zischke and Donald Poskitt)

Biography

Gael Martin is a Professor of Econometrics in the Department of Econometrics and Business Statistics at Monash University, Australia, and a Fellow of the Academy of Social Sciences. She was an Australian Research Council Future Fellow from 2010 to 2013. Her primary research interests have been in the development of simulation-based inferential and forecasting methods for complex dynamic models in economics and finance. Time series models for long memory, non-Gaussian - including discrete count - data have been a particular focus, with state space representations being central to much of that work. The development of Bayesian simulation-based methods has been a very important part of her research output, including work on the newer computational methods such as approximate Bayesian computation. Her interests centre not only on methods of inference, but on the impact of inferential technique on probabilistic forecasting, and the accuracy thereof. Misspecification of the predictive model has been a particular focus of late, including the development of a ‘loss-based’ paradigm for prediction that delivers accurate predictions when the predictive model is wrong. She is currently an Associate Editor of Journal of Applied Econometrics, International Journal of Forecasting (IJF) and Econometrics and Statistics, and was a guest editor for a special issue of IJF on Bayesian Forecasting in Economics.

Gael's personal webpage includes all published work and some other current projects.