Federal Reserve Bank of St. Louis
Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR
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.
Cite this item
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.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
Keywords: Vector autoregression; Blocking model; Stacked vector autoregression; Mixed-frequency estimation; Bayesian methods; Nowcasting; Forecasting
This item with handle RePEc:fip:fedlwp:2015-030
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