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On Model Aggregation and Forecast Combination

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Abstract

Policy makers express their views and decisions via the lens of a particular model or theory. But since any model is a highly stylized representation of the unknowable object of interest, all these models are inherently misspecified, and the resulting ambiguity injects uncertainty in the decision-making process. We argue that entropy-based aggregation is a convenient device to confront this uncertainty and summarize relevant information from a set of candidate models and forecasts. The proposed aggregation tends to robustify the decision-making process to various sources of risks and uncertainty. We find compelling evidence for the advantages of entropy-based aggregation for forecasting inflation.

Suggested Citation

  • Nikolay Gospodinov & Esfandiar Massoumi, 2025. "On Model Aggregation and Forecast Combination," FRB Atlanta Working Paper 2025-12, Federal Reserve Bank of Atlanta.
  • Handle: RePEc:fip:fedawp:101967
    DOI: 10.29338/wp2025-12
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    References listed on IDEAS

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    1. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    2. Andrew J. Patton, 2020. "Comparing Possibly Misspecified Forecasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 796-809, October.
    3. Anisha Ghosh & Christian Julliard & Alex P. Taylor, 2017. "What Is the Consumption-CAPM Missing? An Information-Theoretic Framework for the Analysis of Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 30(2), pages 442-504.
    4. Jing Lei & Max G’Sell & Alessandro Rinaldo & Ryan J. Tibshirani & Larry Wasserman, 2018. "Distribution-Free Predictive Inference for Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1094-1111, July.
    5. Hansen, Bruce E., 2008. "Least-squares forecast averaging," Journal of Econometrics, Elsevier, vol. 146(2), pages 342-350, October.
    6. Gospodinov, Nikolay & Maasoumi, Esfandiar, 2021. "Generalized aggregation of misspecified models: With an application to asset pricing," Journal of Econometrics, Elsevier, vol. 222(1), pages 451-467.
    7. Maasoumi, Esfandiar, 1986. "The Measurement and Decomposition of Multi-dimensional Inequality," Econometrica, Econometric Society, vol. 54(4), pages 991-997, July.
    8. Nikolay Gospodinov & Serena Ng, 2013. "Commodity Prices, Convenience Yields, and Inflation," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 206-219, March.
    9. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
    10. Stutzer, Michael, 1995. "A Bayesian approach to diagnosis of asset pricing models," Journal of Econometrics, Elsevier, vol. 68(2), pages 367-397, August.
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    Keywords

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    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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