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Forecast combination for discrete choice models: predicting FOMC monetary policy decisions

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  • Pauwels, Laurent
  • Vasnev, Andrey

Abstract

This paper provides a methodology for combining forecasts based on several discrete choice models. This is achieved primarily by combining one-step-ahead probability forecast associated with each model. The paper applies well-established scoring rules for qualitative response models in the context of forecast combination. Log-scores and quadratic-scores are both used to evaluate the forecasting accuracy of each model and to combine the probability forecasts. In addition to producing point forecasts, the effect of sampling variation is also assessed. This methodology is applied to forecast the US Federal Open Market Committee (FOMC) decisions in changing the federal funds target rate. Several of the economic fundamentals influencing the FOMC decisions are nonstationary over time and are modelled in a similar fashion to Hu and Phillips (2004a, JoE). The empirical results show that combining forecasted probabilities using scores mostly outperforms both equal weight combination and forecasts based on multivariate models.

Suggested Citation

  • Pauwels, Laurent & Vasnev, Andrey, 2011. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Working Papers 11/2011, University of Sydney Business School, Discipline of Business Analytics.
    • Handle: RePEc:syb:wpbsba:2123/8158
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    Cited by:

    1. Su, Shiwei & Ahmad, Ahmad Hassan & Wood, Justine & Jia, Songbo, 2025. "Monetary policy analysis using natural language processing: Evaluating the People's Bank of China's minutes and report summary with the Taylor Rule," Economic Modelling, Elsevier, vol. 149(C).
    2. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    3. Pauwels, Laurent, 2019. "Predicting China’s Monetary Policy with Forecast Combinations," Working Papers BAWP-2019-07, University of Sydney Business School, Discipline of Business Analytics.
    4. repec:syb:wpbsba:05/2013 is not listed on IDEAS
    5. Jungyeon Yoon & Juanjuan Fan, 2024. "Forecasting the direction of the Fed's monetary policy decisions using random forest," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2848-2859, November.
    6. Pauwels, Laurent & Vasnev, Andrey, 2014. "Forecast combination for U.S. recessions with real-time data," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 138-148.
    7. Kim, Hyerim & Kang, Kyu Ho, 2022. "The Bank of Korea watch," Journal of International Money and Finance, Elsevier, vol. 126(C).

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