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Economic Analysis of the Digital Economy$
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Avi Goldfarb, Shane M. Greenstein, and Catherine E. Tucker

Print publication date: 2015

Print ISBN-13: 9780226206844

Published to Chicago Scholarship Online: September 2015

DOI: 10.7208/chicago/9780226206981.001.0001

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Bayesian Variable Selection for Nowcasting Economic Time Series

Bayesian Variable Selection for Nowcasting Economic Time Series

Chapter:
(p.119) 4 Bayesian Variable Selection for Nowcasting Economic Time Series
Source:
Economic Analysis of the Digital Economy
Author(s):

Steven L. Scott

Hal R. Varian

Publisher:
University of Chicago Press
DOI:10.7208/chicago/9780226206981.003.0004

We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. The motivating example is the use of Google Trends search engine query data as a contemporaneous predictor of economic indicators. Our preferred approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. The Kalman filter can be used to control for time series feature, such as seasonality and trend; the regression can be used to incorporate predictors such as search engine queries; and model averaging can be used to reduce the danger of overfitting. Overall the Bayesian approach allows a flexible way to incorporate prior knowledge, both subjective and objective, into the estimation procedure. We illustrate this approach using search engine query data as predictors for consumer sentiment and gun sales.

Keywords:   forecasting, nowcasting, Bayesian methods

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