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Calculating Interval Forecasts
Journal of Business & Economic Statistics
Vol. 11, No. 2 (Apr., 1993), pp. 121-135
Stable URL: http://www.jstor.org/stable/1391361
Page Count: 15
You can always find the topics here!Topics: Analytical forecasting, Forecasting models, Time series forecasting, Forecasting techniques, Statistical forecasts, Musical intervals, Economic forecasting models, Parametric models, Economic statistics, Statistical models
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The importance of interval forecasts is reviewed. Several general approaches to calculating such forecasts are described and compared. They include the use of theoretical formulas based on a fitted probability model (with or without a correction for parameter uncertainty), various "approximate" formulas (which should be avoided), and empirically based, simulation, and resampling procedures. The latter are useful when theoretical formulas are not available or there are doubts about some model assumptions. The distinction between a forecasting method and a forecasting model is expounded. For large groups of series, a forecasting method may be chosen in a fairly ad hoc way. With appropriate checks, it may be possible to base interval forecasts on the model for which the method is optimal. It is certainly unsound to use a model for which the method is not optimal, but, strangely, this is sometimes done. Some general comments are made as to why prediction intervals tend to be too narrow in practice to encompass the required proportion of future observations. An example demonstrates the overriding importance of careful model specification. In particular, when data are "nearly nonstationary," the difference between fitting a stationary and a nonstationary model is critical.
Journal of Business & Economic Statistics © 1993 American Statistical Association