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Using Forecasts To Uncover The Loss Function Of Federal Open Market Committee Members

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  • Pierdzioch, Christian
  • Rülke, Jan-Christoph
  • Tillmann, Peter

Abstract

We revisit the sources of the bias in Federal Reserve forecasts and assess whether a precautionary motive can explain the forecast bias. In contrast to the existing literature, we use forecasts submitted by individual Federal Open Market Committee (FOMC) members to uncover members' implicit loss function. Our key finding is that the loss function of FOMC members is asymmetric: FOMC members incur a higher loss when they underpredict (overpredict) inflation and unemployment (nominal and real growth) as compared to their making an overprediction (underprediction) of similar size. We also find that an asymmetric loss function, in some cases, weakens evidence against forecast rationality, though results depend on the variable being projected and the subgroup of FOMC members being studied. Furthermore, we add to the recent controversy on the relative quality of FOMC forecasts compared to staff forecasts. Our results suggest that differences in predictive ability could indeed stem from differences in preferences.

Suggested Citation

  • Pierdzioch, Christian & Rülke, Jan-Christoph & Tillmann, Peter, 2016. "Using Forecasts To Uncover The Loss Function Of Federal Open Market Committee Members," Macroeconomic Dynamics, Cambridge University Press, vol. 20(3), pages 791-818, April.
    • Handle: RePEc:cup:macdyn:v:20:y:2016:i:03:p:791-818_00
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    Cited by:

    1. Arai, Natsuki, 2023. "The FOMC’s new individual economic projections and macroeconomic theories," Journal of Banking & Finance, Elsevier, vol. 151(C).
    2. Yoichi Tsuchiya, 2022. "Evaluating plant managers’ production plans over business cycles: asymmetric loss and rationality," SN Business & Economics, Springer, vol. 2(8), pages 1-29, August.
    3. Pierdzioch, Christian & Reid, Monique B. & Gupta, Rangan, 2016. "Forecasting the South African inflation rate: On asymmetric loss and forecast rationality," Economic Systems, Elsevier, vol. 40(1), pages 82-92.
    4. Behrens, Christoph & Pierdzioch, Christian & Risse, Marian, 2018. "Testing the optimality of inflation forecasts under flexible loss with random forests," Economic Modelling, Elsevier, vol. 72(C), pages 270-277.
    5. Yoichi Tsuchiya, 2021. "The value added of the Bank of Japan's range forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 817-833, August.

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