Uncertain kingdom: nowcasting GDP and its revisions

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 aim, we assemble a novel dataset of monthly and quarterly indicators featuring over ten years of real-time data vintages. 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, leading to the release-augmented DFM (RA-DFM). 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. By evaluating the real-time performance of the RA-DFM, we find that the model’s 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 UK GDP growth real-time estimates are commensurate with professional nowcasters. We also provide evidence that statistical office data on production and labour markets, subject to large publication delays, account for most of the forecastability of the revisions.