With Scott Brave and Alejandro Justiniano.
Published in International Journal of Forecasting, 35(4), 2019: 1692-1707. https://doi.org/10.1016/j.ijforecast.2019.02.010
Previously distributed as Federal Reserve Bank of Chicago Working Paper, No. 2016-05.
Abstract: Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate a large number of time series observed at different intervals into forecasts of economic activity. This paper benchmarks the performance of MF-BVARs in forecasting U.S. real Gross Domestic Product growth relative to surveys of professional forecasters and documents the influence of certain specification choices. We find that a medium-large MF-BVAR provides an attractive alternative to surveys at the medium term forecast horizons of interest to central bankers and private sector analysts. Furthermore, we demonstrate that certain specification choices such as model size, prior selection mechanisms, and modeling in levels versus growth rates strongly influence its performance.
The working paper version of the article is available as a pdf. (37 pages, 929 KB)
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