Bayesian Variable Selection for Nowcasting Economic Time Series

Steven L. Scott and Hal Varian ()

No 19567, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: C11 C53 (search for similar items in EconPapers)
Date: 2013-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (21)

Published as Bayesian Variable Selection for Nowcasting Economic Time Series , Steven L. Scott, Hal R. Varian. in Economic Analysis of the Digital Economy , Goldfarb, Greenstein, and Tucker. 2015

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Related works:
Chapter: Bayesian Variable Selection for Nowcasting Economic Time Series (2015)
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