Predicting the Present with Bayesian Structural Time Series

Posted: 1 Aug 2013

See all articles by Steven L. Scott

Steven L. Scott

Google Inc.

Hal R. Varian

University of California, Berkeley - School of Information; University of California, Berkeley - Operations and Information Technology Management Group; National Bureau of Economic Research (NBER)

Date Written: June 28, 2013

Abstract

This article describes a system for short term forecasting based on an ensemble prediction that averages over different combinations of predictors. The system combines a structural time series model for the target series with regression component capturing the contributions of contemporaneous search query data. A spike-and-slab prior on the regression coefficients induces sparsity, dramatically reducing the size of the regression problem. Our system averages over potential contributions from a very large set of models and gives easily digested reports of which coefficients are likely to be important. We illustrate with applications to initial claims for unemployment benefits and to retail sales. Although our exposition focuses on using search engine data to forecast economic time series, the underlying statistical methods can be applied to more general short term forecasting with large numbers of contemporaneous predictors.

Keywords: forecasting, Bayesian methods, model selection

Suggested Citation

Scott, Steven L. and Varian, Hal R., Predicting the Present with Bayesian Structural Time Series (June 28, 2013). Available at SSRN: https://ssrn.com/abstract=2304426 or http://dx.doi.org/10.2139/ssrn.2304426

Steven L. Scott

Google Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

Hal R. Varian (Contact Author)

University of California, Berkeley - School of Information ( email )

102 South Hall
Berkeley, CA 94720-4600
United States
510-642-9980 (Phone)
510-642-5814 (Fax)

HOME PAGE: http://www.sims.berkeley.edu/~hal/people/hal/biography.html

University of California, Berkeley - Operations and Information Technology Management Group ( email )

545 Student Services Building
Berkeley, CA 94720
United States
510-643-6388 (Phone)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
473
rank
57,164
Abstract Views
1,710
PlumX Metrics