Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR

Michael McCracken, Michael Owyang and Tatevik Sekhposyan

No 2015-030, Working Papers from Federal Reserve Bank of St. Louis

Abstract: We use a mixed-frequency vector autoregression to obtain intraquarter point and density forecasts as new, high frequency information becomes available. This model, delineated in Ghysels (2016), is specified at the lowest sampling frequency; high frequency observations are treated as different economic series occurring at the low frequency. As this type of data stacking results in a high-dimensional system, we rely on Bayesian shrinkage to mitigate parameter proliferation. We obtain high-frequency updates to forecasts by treating new data releases as conditioning information. The same framework is used for scenario analysis to obtain forecasts conditional on a hypothetical future path of the variables in the system. We show that the methodology results in competitive point and density forecasts and illustrate the usefulness of the methodology by providing forecasts of real GDP growth given hypothetical paths of a central bank policy rate.

Keywords: Stacked vector autoregression; Mixed-frequency estimation; Vector autoregression; Bayesian methods; Forecasting; Nowcasting; conditional forecasts (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2015-10-08, Revised 2020-04-10
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mst and nep-ore
Note: Publisher URL: https://www.ijcb.org/journal/ijcb21q5a8.pdf
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Citations: View citations in EconPapers (9)

Published in International Journal of Central Banking

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Journal Article: Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR (2021)
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DOI: 10.20955/wp.2015.030

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