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Federal Reserve Bank of St. Louis
Working Papers
Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR
Michael W. McCracken
Michael T. Owyang
Tatevik Sekhposyan
Abstract

We assess point and density forecasts from a mixed-frequency vector autoregression (VAR) to obtain intra-quarter forecasts of output growth as new information becomes available. The econometric model is specified at the lowest sampling frequency; high frequency observations are treated as different economic series occurring at the low frequency. We impose restrictions on the VAR to account explicitly for the temporal ordering of the data releases. Because this type of data stacking results in a high-dimensional system, we rely on Bayesian shrinkage to mitigate parameter proliferation. The relative performance of the model is compared to forecasts from various time-series models and the Survey of Professional Forecaster's. We further illustrate the possible usefulness of our proposed VAR for causal analysis.


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Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR, Federal Reserve Bank of St. Louis, Working Papers 2015-30, 08 Oct 2015.
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Keywords: Vector autoregression; Blocking model; Stacked vector autoregression; Mixed-frequency estimation; Bayesian methods; Nowcasting; Forecasting
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