On the Forecast Combination Puzzle

A Machine Learning Approach to the Forecast Combination Puzzle

Forecast combination algorithms provide a robust solution to noisy data and shifting process dynamics. However in practice, sophisticated combination methods often fail to consistently outperform the simple mean combination. This “forecast combination puzzle” limits the adoption of alternative combination approaches and forecasting algorithms by policy-makers. Through an adaptive machine learni...

A Simple Theoretical Explanation of the Forecast Combination Puzzle

This paper offers a theoretical explanation why forecast combination with the estimated optimal weights has often poor performance in applications. The explanation is simple. The properties of the combination are often derived under the assumption that the weights are fixed while in practice they have to be estimated. If the weight estimation is taken into account during the optimality derivati...

Density Forecast Combination

In this paper we investigate whether and how far density forecasts sensibly can be combined to produce a “better” pooled density forecast. In so doing we bring together two important but hitherto largely unrelated areas of the forecasting literature in economics, density forecasting and forecast combination. We provide simple Bayesian methods of pooling information across alternative density fo...

Forecast Evaluation and Combination

Forecast Combination across Estimation Windows∗

This paper considers the problem of forecast combination when forecasts are generated from the same model but use different estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or more structural breaks. The analysis is then extended to a linear regression model with an exogenous regressor. It is shown that compared to foreca...