Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth

Nikolaus Hautsch and Fuyu Yang

No 56, University of East Anglia Applied and Financial Economics Working Paper Series from School of Economics, University of East Anglia, Norwich, UK.

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.

Date: 2014-01
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mac and nep-sog
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