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.
https://doi.org/10.20955/wp.2015.030
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