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Federal Reserve Bank of Chicago
Working Paper Series
Forecasting Economic Activity with Mixed Frequency Bayesian VARs
Scott Brave
R. Andrew Butters
Alejandro Justiniano
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.


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Scott Brave & R. Andrew Butters & Alejandro Justiniano, Forecasting Economic Activity with Mixed Frequency Bayesian VARs, Federal Reserve Bank of Chicago, Working Paper Series WP-2016-5, 20 May 2016.
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Keywords: Mixed frequency; Bayesian VAR; Real-time data; Nowcasting
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