Forecasting and nowcasting macroeconomic variables: a methodological overview

We consider the reasons for nowcasting, how nowcasts can be achieved, and the use and timing of information. The existence of contemporaneous data such as surveys is a major difference from forecasting, but many of the recent lessons about forecasting remain relevant. Given the extensive disaggregation over variables underlying flash estimates of aggregates, we show that automatic model selection can play a valuable role, especially when location shifts would otherwise induce nowcast failure. Thus, we address nowcasting when location shifts occur, probably with measurement error. We describe impulse-indicator saturation as a potential solution to such shifts, noting its relation to intercept corrections and to robust methods to avoid systematic nowcast failure. We propose a nowcasting strategy, building models of all disaggregate series by automatic methods, forecasting all variables before the end of each period, testing for shifts as available measures arrive, and adjusting forecasts of cognate missing series if substantive discrepancies are found. An alternative is switching to robust forecasts when breaks are detected. We apply a variant of this strategy to nowcast UK GDP growth, seeking pseudo real-time data availability.