A subjective change that a forecaster makes to a forecast produced by a model. Making such changes is controversial. In psychology, extensive research on cross-sectional data led to the conclusion that one should not subjectively adjust forecasts from a quantitative model. Meehl (1954) summarized a long stream of research on personnel selection and concluded that employers should not meet job candidates because that would lead them to improperly adjust a model’s prediction as to their success. In contrast, studies on economic time series show that judgmental adjustments sometimes help, although mechanical adjustments seem to do as well. Armstrong (1985, pp. 235-238) summarizes seven studies on this issue. The key is to identify the conditions under which to make adjustments. Adjustments seem to improve accuracy when the expert has knowledge about the level. Judgmental adjustments are common. According to Sanders and Mandrodt’s (1990) survey of forecasters at 96 US corporations, about 45% of the respondents claimed that they always made judgmental adjustments to statistical forecasts, while only 9% said that they never did. The main reasons the respondents gave for revising quantitative forecasts were to incorporate “knowledge of the environment” (39%), “product knowledge” (30%), and “past experience” (26%). While these reasons seem sensible, such adjustments are often made by biased experts. In a survey of members of the International Institute of Forecasters, 269 respondents were asked whether they agreed with the following statement: “Too often, company forecasts are modified because of political considerations.” On a scale from 1 = “disagree strongly” to 7 = “agree strongly,” the mean response was 5.4. (Details on the survey are provided in Yokum and Armstrong 1995.) In Fildes and Hastings’ (1994) survey of 45 managers in a large conglomerate, 64% of them responded “forecasts are frequently politically motivated.” For a discussion on principles for making subjective adjustments of extrapolations, see Sanders and Ritzman (2001).
- Meehl, P. (1954), Clinical versus Statistical
Prediction. Minneapolis: University of Minnesota
Press.
- Armstrong, J. S. (1985), Long-Range Forecasting. New York: John Wiley. (Full
text)
- Sanders, N. R. & K. B. Mandrodt (1990), “Forecasting
practices in U. S. corporations,” Interfaces, 24 (2),
92-100.
- Yokum, J. & J. S. Armstrong (1995), "Beyond
Accuracy: Comparison of Criteria Used to Select Forecasting
Methods," International Journal of Forecasting,
11, 591-597. (Full text)
- Fildes, R. & R. Hastings, (1994), “The organization
and improvement of market forecasting,” Journal of the Operational Research
Society, 45, 1-16.
- Sanders, N. R. & L. Ritzman (2001), “Judgmental
adjustments of statistical forecasts,” in J. S. Armstrong (ed.), Principles
of Forecasting. Norwell, MA: Kluwer Academic Press.