Corrected Forecast Combinations
Chu-An Liu, Andrey L. Vasnev

TL;DR
This paper introduces a simple correction method for forecast combinations with serially dependent errors, significantly improving forecast accuracy and addressing the forecast combination puzzle.
Contribution
It formalizes a correction approach within the conditional risk framework and demonstrates its effectiveness using macroeconomic forecast data.
Findings
Correction improves forecast accuracy in macroeconomic data
A coefficient around 0.5 is a robust correction factor
Mitigates the forecast combination puzzle in practice
Abstract
This paper proposes corrected forecast combinations when the original combined forecast errors are serially dependent. Motivated by the classic Bates and Granger (1969) example, we show that combined forecast errors can be strongly autocorrelated and that a simple correction--adding a fraction of the previous combined error to the next-period combined forecast--can deliver sizable improvements in forecast accuracy, often exceeding the original gains from combining. We formalize the approach within the conditional risk framework of Gibbs and Vasnev (2024), in which the combined error decomposes into a predictable component (measurable at the forecast origin) and an innovation. We then link this correction to efficient estimation of combination weights under time-series dependence via GLS, allowing joint estimation of weights and an error-covariance structure. Using the U.S. Survey of…
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Taxonomy
TopicsMonetary Policy and Economic Impact · Forecasting Techniques and Applications · Market Dynamics and Volatility
