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A1058
Title: Real-time forecasting with a large, mixed frequency, Bayesian VAR Authors:  Michael McCracken - Federal Reserve Bank of Saint Louis (United States)
Michael Owyang - Federal Reserve Bank of St Louis (United States)
Tatevik Sekhposyan - Texas A and M University (United States) [presenting]
Abstract: Point forecasts from a large, mixed-frequency, structural vector autoregression (VAR) are assessed. The VAR we consider uses data at monthly and quarterly frequencies to obtain forecasts of low frequency variables such as output growth on a more frequent basis. The structure imposed on the VAR allows us to account for the temporal ordering of the data explicitly, thus accounting for the effects of temporal surprises across the variables in a more interpretable manner. Our framework relies on a blocking model, i.e. econometric model specified at a low frequency, where high frequency observations of a particular variable are stacked, i.e. treated as individual economic series occurring at the low frequency. Since stacking results in a high-dimensional system of equations, we rely on Bayesian shrinkage techniques to mitigate parameter proliferation. We use our model for short-term forecasting of the U.S. economy, as well as for structural analysis. The relative performance of the model is compared to the factor model and private sector forecasts.