Conditionally optimal weights and forward-looking approaches to combining forecasts

Christopher Gibbs and Andrey Vasnev

International Journal of Forecasting, 2024, vol. 40, issue 4, 1734-1751

Abstract: In forecasting, there is a tradeoff between in-sample fit and out-of-sample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, information is often withheld from a forecast to prevent over-fitting the data. We show that one way to exploit this information is through forecast combination. Optimal combination weights in this environment minimize the conditional mean squared error that balances the conditional bias and the conditional variance of the combination. The bias-adjusted conditionally optimal forecast weights are time varying and forward looking. Real-time tests of conditionally optimal combinations of model-based forecasts and surveys of professional forecasters show significant gains in forecast accuracy relative to standard benchmarks for inflation and other macroeconomic variables.

Keywords: Forecast combination; Conditionally optimal weights; Forecast combination puzzle; Inflation; Phillips curve (search for similar items in EconPapers)
Date: 2024
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Working Paper: Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:4:p:1734-1751

DOI: 10.1016/j.ijforecast.2024.03.002

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