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Bayesian Variable Selection for Nowcasting Economic Time Series

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.
Year of publication:
2013-10
Authors: Varian, Hal ; Scott, Steven L.
Institutions: National Bureau of Economic Research (NBER)
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application/pdf
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Type of publication: Book / Working Paper
Notes:
PR published as <a href="http://www.nber.org/chapters/c12995">Bayesian Variable Selection for Nowcasting Economic Time Series</a>, Steven L. Scott, Hal R. Varian. in <a href="http://www.nber.org/books/gree13-1">Economic Analysis of the Digital Economy</a>, Goldfarb, Greenstein, and Tucker. 2015 The price is Paper copy available by mail Number 19567
Classification: C11 - Bayesian Analysis ; C53 - Forecasting and Other Model Applications
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Persistent link: https://www.econbiz.de/10011264930
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