Title: Forecasting in the Presence of Structural Breaks and Policy Regime Shifts
Abstract: When no model coincides with a nonconstant data generation process, forecast failure occurs, and noncausal statistical devices may provide the best available forecasts: examples include intercept corrections and differenced-data VARs. However, such models are not a reliable basis for economic policy analyses and may even have no policy implications. Indeed, a "paradox" can result if their forecasts induce policy changes, which in turn alter the data outcome. This suggests correcting statistical forecasts by using the econometric model's estimate of the "scenario" change, and doing so is shown to yield reduced forecast-error biases.