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 …

__Abstract__ The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects … for forecasting weekly and monthly horizons. …

installed based information and provide several ways in which installed base forecasting can be used. We discuss cases of … installed based forecasting at four companies and list the issues involved. Moreover, we provide some models to assess the value …