Staff Working Paper No. 764
By Nikoleta Anesti, Ana Beatriz Galvão and Silvia Miranda-Agrippino
We design a new econometric framework to nowcast macroeconomic data subject to revisions, and use it to predict UK GDP growth in real-time. To this end, we assemble a novel dataset of monthly and quarterly indicators featuring over ten years of real-time data vintages. In the Release-Augmented DFM (or RA-DFM) successive monthly estimates of GDP growth for the same quarter are treated as correlated observables in a Dynamic Factor Model (DFM) that also includes a large number of mixed-frequency predictors. The framework allows for a simple characterisation of the stochastic process for the revisions as a function of the observables, and permits a detailed assessment of the contribution of the data flow in informing (i) forecasts of quarterly GDP growth; (ii) the evolution of forecast uncertainty; and (iii) forecasts of revisions to early released GDP data. We find that the RA-DFM predictions have information about the latest GDP releases above and beyond that contained in the statistical office earlier estimates; predictive intervals are well-calibrated; and that real-time estimates of UK GDP growth are commensurate with those of professional forecasters. Data on production and labour markets, subject to large publication delays, account for most of the forecastability of the revisions.