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.
Working Papers,
No. 2016-05,
2016
Forecasting Economic Activity with Mixed Frequency Bayesian VARs (Revised July 2018)
