inflation forecasting.  The model generalises to unobserved component models where Gaussian shocks are replaced by martingale …

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal …-error taxonomy for factor models highlights the impacts of location shifts on forecast-error biases.  Forecasting US GDP over 1-, 4 … for nowcasting or short-term forecasting, but their relative performance declines as the forecast horizon increases …

triangle.  It is shown that methods for forecasting non-stationary time series are helpful.  We illustrate the method using …

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 …, forecasting all variables before the end of each period, testing for shifts as available measures arrive, and adjusting forecasts …

This paper describes some recent advances and contributions to our understanding of economic forecasting. The framework … we develop helps explain the findings of forecasting competitions and the prevalence of forecast failure. It constitutes …

series, there is no clear consensus regarding the forecasting abilities of these models. In this paper we develop a general … approach to predict multiple time series subject to Markovian shifts in the regime. The feasibility of the proposed forecasting …

We defend the forecasting performance of the FOMC from the recent criticism of Christina and David Romer.  Our argument …