Forecasting Economic Activity with Mixed Frequency Bayesian Vars

43 Pages Posted: 22 Jun 2016 Last revised: 29 Apr 2020

See all articles by Scott A. Brave

Scott A. Brave

Federal Reserve Bank of Chicago

R. Andrew Butters

Indiana University

Alejandro Justiniano

Federal Reserve Bank of Chicago

Date Written: 2016-05-20

Abstract

Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate a large number of mixed frequency indicators into forecasts of economic activity. This paper evaluates the forecast performance of MF-BVARs relative to surveys of professional forecasters and investigates the influence of certain specification choices on this performance. We leverage a novel real-time dataset to conduct an out-of-sample forecasting exercise for U.S. real gross domestic product (GDP). MF-BVARs are shown to provide an attractive alternative to surveys of professional forecasters for forecasting GDP growth. However, certain specification choices such as model size and prior selection can affect their relative performance.

Keywords: Mixed frequency, Bayesian VAR, Real-time data, Nowcasting

JEL Classification: C32, C53, E37

Suggested Citation

Brave, Scott A. and Butters, R. and Justiniano, Alejandro, Forecasting Economic Activity with Mixed Frequency Bayesian Vars (2016-05-20). FRB of Chicago Working Paper No. WP-2016-5, Available at SSRN: https://ssrn.com/abstract=2799139

Scott A. Brave (Contact Author)

Federal Reserve Bank of Chicago ( email )

230 South LaSalle Street
Chicago, IL 60604
United States

R. Butters

Indiana University ( email )

1309 E. Tenth St.
Bloomington, IN 47405
United States

HOME PAGE: http://https://kelley.iu.edu/BEPP/faculty/page14113.cfm?ID=46947

Alejandro Justiniano

Federal Reserve Bank of Chicago ( email )

230 South LaSalle Street
Chicago, IL 60604
United States

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