What is it about?

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.

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Why is it important?

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.

Perspectives

In memoriam: Scott and Andrew would like to dedicate this paper to our dear departed friend and colleague Alejandro Justiniano. May he rest in peace knowing that his influence on us and everyone that he interacted with in the economics profession will leave a lasting impression on the discipline that he so passionately pursued.

Scott Brave
Federal Reserve Bank of Chicago

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This page is a summary of: Forecasting economic activity with mixed frequency BVARs, International Journal of Forecasting, October 2019, Elsevier, DOI: 10.1016/j.ijforecast.2019.02.010.
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