Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/172921
Authors: 
Brave, Scott A.
Butters, R. Andrew
Justiniano, Alejandro
Year of Publication: 
2016
Series/Report no.: 
Working Paper 2016-05
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.
Subjects: 
mixed frequency
Bayesian VAR
real-time data
nowcasting
JEL: 
C32
C53
E37
Document Type: 
Working Paper
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