Real-Time Forecasting With a Mixed-Frequency VAR

This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies-quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a...

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Bibliographic Details
Published in:Journal of business & economic statistics 2015-07, Vol.33 (3), p.366-380
Main Authors: Schorfheide, Frank, Song, Dongho
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian methods
Econometrics
Economic forecasting
Hyperparameter selection
Macroeconomic forecasting
Marginal data density
Minnesota prior
Real time
Real-time data
Regression analysis
Studies
Vector autoregressions
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Summary:This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies-quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.
ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.2014.954707