Forecasting Economic Activity with Mixed Frequency Bayesian VARs

Scott Brave, R. Andrew Butters and Alejandro Justiniano

No WP-2016-5, Working Paper Series from Federal Reserve Bank of Chicago

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 VARs; Real-time data; Nowcasting (search for similar items in EconPapers)
JEL-codes: C32 C53 E37 (search for similar items in EconPapers)
Pages: 73 pages
Date: 2016-05-20
New Economics Papers: this item is included in nep-ets, nep-for, nep-mac and nep-ore
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Citations: View citations in EconPapers (16)

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