Bayesian Variable Selection for Nowcasting Economic Time Series
NBER Working Paper No. 19567
We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine query data as predictors for consumer sentiment and gun sales.
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Forthcoming as Bayesian Variable Selection for Nowcasting Economic Time Series, Steven L. Scott, Hal Varian, in Economics of Digitization (2014), University of Chicago Press
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