Forecasting economic activity with mixed frequency BVARs

Scott Brave, R. Andrew Butters and Alejandro Justiniano

International Journal of Forecasting, 2019, vol. 35, issue 4, 1692-1707

Abstract: Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate large numbers of time series that are observed at different intervals into forecasts of economic activity. This paper benchmarks the performances of MF-BVARs for forecasting U.S. real gross domestic product growth against surveys of professional forecasters and documents the influences of certain specification choices. We find that a medium–large MF-BVAR provides an attractive alternative to surveys at the medium-term forecast horizons that are of interest to central bankers and private sector analysts. Furthermore, we demonstrate that certain specification choices influence its performance strongly, such as model size, prior selection mechanisms, and modeling in levels versus growth rates.

Keywords: Mixed frequency; Bayesian VAR; Real-time data; Nowcasting; Forecasting; Economic activity (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1692-1707

DOI: 10.1016/j.ijforecast.2019.02.010

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