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Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth

Author

Listed:
  • Nikolaus Hautsch

    (University of Vienna)

  • Fuyu Yang

    (University of East Anglia)

Abstract

We propose a Bayesian framework for nowcasting GDP growth in real time. Using vintage data on macroeconomic announcements we set up a state space system connecting latent GDP growth rates to agencies' releases of GDP and other economic indicators. We propose a Gibbs sampling scheme to filter out daily GDP growth rates using all available macroeconomic information. The sample draws from the resulting posterior distri#bution, thereby allowing us to simulate backcasting, nowcasting, and forecasting densities. A stochastic search variable selection procedure yields a data-driven way of selecting the relevant GDP predictors out of a potentially large set of economic indicators.

Suggested Citation

  • Nikolaus Hautsch & Fuyu Yang, 2014. "Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth," University of East Anglia Applied and Financial Economics Working Paper Series 056, School of Economics, University of East Anglia, Norwich, UK..
    • Handle: RePEc:uea:aepppr:2012_56
    as

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    File URL: https://ueaeco.github.io/working-papers/papers/afe/UEA-AFE-056.pdf
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    References listed on IDEAS

    as
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