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Forecasting economic activity by Bayesian bridge model averaging

Author

Listed:
  • Lorenzo Bencivelli

    () (Bank of Italy)

  • Massimiliano Marcellino

    () (Bocconi University, IGIER and CEPR)

  • Gianluca Moretti

    () (Bank of America Merril Lynch)

Abstract

This paper proposes the use of Bayesian model averaging (BMA) as an alternative tool to forecast GDP relative to simple bridge models and factor models. BMA is a computationally feasible method that allows us to explore the model space even in the presence of a large set of candidate predictors. We test the performance of BMA in now-casting by means of a recursive experiment for the euro area and the three largest countries. This method allows flexibility in selecting the information set month by month. We find that BMA-based forecasts produce smaller forecast errors than standard bridge model when forecasting GDP in Germany, France and Italy. At the same time, it also performs as well as medium-scale factor models when forecasting Eurozone GDP.

Suggested Citation

  • Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2017. "Forecasting economic activity by Bayesian bridge model averaging," Empirical Economics, Springer, vol. 53(1), pages 21-40, August.
  • Handle: RePEc:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1199-9
    DOI: 10.1007/s00181-016-1199-9
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    2. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    3. Stolbov, Mikhail & Shchepeleva, Maria, 2020. "What predicts the legal status of cryptocurrencies?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 273-291.

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