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Composite Models Reduce Forecast Errors: The Case of New Automobile Sales 1979-1983
Howard Keen Jr.
Vol. 19, No. 4 (July 1984), pp. 47-50
Published by: Palgrave Macmillan Journals
Stable URL: http://www.jstor.org/stable/23483742
Page Count: 4
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Most business forecasters attempt to incorporate all relevant available information into their projections. One systematic way of doing this is by combining predictions from two or more sources into a formal composite model. This paper reports the results of combining projections of new car sales from Chase Econometrics with those of a univariate Box-Jenkins model. Although Box-Jenkins models tend to produce relatively less accurate predictions in highly volatile periods, such as that of 1979-1983, combining those predictions with the Chase forecasts nevertheless resulted in smaller errors than those of Chase alone.
Business Economics © 1984 Palgrave Macmillan Journals