Summary
In spite of widespread criticism, 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 preliminaryestimates subject to a revision process, the estimators of the coefficients are affected by the presence of the preliminary data, the projections for the exogenous variables are affected 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 finalized, in this paper they are seen as repeated predictions of the same quantity (referring to different information sets not necessarily overlapping with one 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 final data and contain systematic effects.
Similar content being viewed by others
References
Brown, W. andR. S. Mariano, 1989, Predictors in Dynamic Nonlinear Models: Large-sample Behavior,Econometric Theory.
Chong, Y. C. andD. F. Hendry, 1986, Econometric Evaluation of Linear Macro-Economic Models,Review of Economic Studies, 53, 671–690.
Corrado, C. andM. Greene, 1988, Reducing Uncertainty in Short-term Projections: Linkage of Monthly and Quarterly Models,Journal of Forecasting, 7, 77–102.
Corrado, C. andJ. Haltmaier, 1988, The Use of High-Frequency Data in Model-based Forecasting at the Federal Reserve Board, Finance and Economics Discussion Series n. 24, Federal Researve Board, Washington D. C.
Diebold, F. X., 1988, Serial Corelation and the Combination of Forecasts,Journal of Business and Economic Statistics, 6, 105–111.
Fair, R., 1980, Estimating the Expected Predictive Accuracy of Econometric Models,International Economic Review, 21, 355–378.
Gallo, G. M., 1971, Forecast Error Decomposition in a Nonlinear Model with Data Revisions,Anales d'Economie et de Statistique, 22, 103–128.
Gallo, G. M. andM. Marcellino, 1996, In Plato's Cave: Sharpening the Shadows of Monetary Announcements, WP ECO 96/29, Europan University Institute.
Granger, C. W. J. andR. Ramanathan, 1984, Improved Method of Combining Forecasts,Journal of Forecasting, 3, 197–204.
Green, M. K., E. P. Howrey andS. H. Hymans, 1986, The Use of Outside Information in Econometric Forecasting, inModel Reliability ed. by D. A. Belsley and E. Kuh, Cambridge Mass.: MIT Press.
de Jong, P., 1987, Rational Economic Data Revisions,Journal of Business and Economic Statistics, 5, 539–548.
Mankiw, N. G., D. E. Runkle andM. D. Shapiro, 1984, Are Preliminary Announcements of the Money Stock Rational Forecasts?,Journal of Monetary Economics, 14, 15–27.
Mariano, R. S. andW. Brown, 1983, Asymptotic Behavior of Predictors in a Nonlinear System,International Economic Review, 24, 523–536.
Author information
Authors and Affiliations
Additional information
Economics Department European University Institute
Thanks are due to my Ph. D. thesis advisor Bobby Mariano for his guidance and encouragment at various stages of this research. The comments of the participants in the Europan Meeting of the Econometric Society in Maastricht, Aug. 1994, helped in improving the presentation,. A grant from the NSF (SES 8604219) is gratefully acknowledged.
Rights and permissions
About this article
Cite this article
Gallo, G.M. Forecast uncertainty reduction in nonlinear models. J. It. Statist. Soc. 5, 73–98 (1996). https://doi.org/10.1007/BF02589583
Issue Date:
DOI: https://doi.org/10.1007/BF02589583