Real-Time Forecasting with a Mixed-Frequency VAR

50 Pages Posted: 10 Dec 2013 Last revised: 13 May 2023

See all articles by Frank Schorfheide

Frank Schorfheide

University of Pennsylvania - Department of Economics; Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER); University of Pennsylvania - The Penn Institute for Economic Research (PIER)

Dongho Song

Johns Hopkins University - Carey Business School

Date Written: December 2013

Abstract

This paper 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 data set, 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.

Suggested Citation

Schorfheide, Frank and Song, Dongho, Real-Time Forecasting with a Mixed-Frequency VAR (December 2013). NBER Working Paper No. w19712, Available at SSRN: https://ssrn.com/abstract=2366046

Frank Schorfheide (Contact Author)

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
133 South 36th Street
Philadelphia, PA 19104-6297
United States

HOME PAGE: http://www.econ.upenn.edu/~schorf

Centre for Economic Policy Research (CEPR)

London
United Kingdom

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
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University of Pennsylvania - The Penn Institute for Economic Research (PIER) ( email )

Philadelphia, PA
United States

Dongho Song

Johns Hopkins University - Carey Business School ( email )

Baltimore, MD 20036-1984
United States