Forecast Uncertainty Reduction in Nonlinear Models

In spite of widespread criticisms, macroeconometric models are still most popular for forecasting and policy analysis. When the most recent data available on both the exogenous and the endogenous variable are preliminary estimates subject to a revision process the estimators of the coeecients are aaected by the presence of the preliminary data, the projections for the exogenous variables are aaected by the presence of data uncertainty, the values of lagged dependent variables used as initial values for forecasts are still subject to revisions. Since several provisional estimates of the value of a certain variable are available before the data are nalized, in this paper they are seen as repeated predictions of the same quantity (referring to diierent information sets not necessarily overlapping with each other) to be exploited in a forecast combination framework. The components of the asymptotic bias and of the asymptotic mean square prediction error related to data uncertainty can be reduced or eliminated by using a forecast combination technique which makes the deterministic and the Monte Carlo predictors not worse than either predictor used with or without provisional data. The precision of the forecast with the nonlinear model can be improved if the provisional data are not rational predictions of the nal data and contain systematic eeects. Thanks are due to participants in the European Meeting of the Econometric Society in Maastricht, Aug. 1994, whose comments helped in improving the presentation. A grant from the NSF (SES 8604219) is gratefully acknowledged.