Principles of Forecasting pp 107-123
Decomposition for Judgmental Forecasting and Estimation
Forecasters often need to estimate uncertain quantities, but with limited time and resources. Decomposition is a method for dealing with such problems by breaking down (decomposing) the estimation task into a set of components that can be more readily estimated, and then combining the component estimates to produce a target estimate. Estimators can effectively apply decomposition to either multiplicative or segmented forecasts, though multiplicative decomposition is especially sensitive to correlated errors in component values. Decomposition is most used for highly uncertain estimates, such as ones having a large numerical value (e.g., millions or more) or quantities in an unfamiliar metric. When possible, multiple estimators should be used and the results aggregated. In addition, multiple decompositions can be applied to the same estimation problem and the results resolved into a single estimate. Decomposition should be used only when the estimator can make component estimates more accurately or more confidently than the target estimate.
KeywordsAlgorithmic decomposition judgmental forecasting numerical estimation
Unable to display preview. Download preview PDF.
- Arkes, H. R. (2001), “Overconfidence in judgmental forecasting,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.Google Scholar
- Armstrong, J. S. (1985), Long-Range Forecasting: From Crystal Ball to Computer (2nd ed.) New York: John Wiley & Sons. (Full text at http://www.hops.wharton.upenn.edu/forecast.)
- Bonner, S. E., R. Libby & M. W. Nelson (1996), “Using decision aids to improve auditors’ conditional probability judgments,” The Accounting Review, 71, 221–240.Google Scholar
- Dawes, R. M. (1975), “The mind, the model, and the task,” in F. Restie, R. M. Shiffron, N. J. Castellan, H. R. Lindman & D. B. Pisoni (eds.), Cognitive Theory. (Vol. 1, ), Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 119–129.Google Scholar
- Edwards, W. & D. von Winterfeldt (1986), Decision Analysis and Behavioral Research. New York: Cambridge University Press.Google Scholar
- Gordon, T. P., J. S. Morris & B. J. Dangerfield (1997), “Top-down or bottom-up: Which is the best approach to forecasting?” Journal of Business Forecasting, 16, 13–16Google Scholar
- Harvey, N. (2001), “Improving judgment in forecasting,” in J. S. Armstrong (ed.), Princi-ples of Forecasting. Norwell, MA: Kluwer Academic Publishers.Google Scholar
- Kahneman, D., P. Slovic & A. Tversky (1982), Judgment Under Uncertainty: Heuristics and Biases. New York: Cambridge University Press.Google Scholar
- Kleinmuntz, D. N., M. G. Fennema & M. E. Peecher (1996), “Conditioned assessment of subjective probabilities: Identifying the benefits of decomposition Organizational Behavior and Human Decision Processes,66, 1–15.Google Scholar
- Plous, S. (1993), The Psychology of Judgment and Decision Making. New York: McGraw-Hill.Google Scholar
- Raiffa, H. (1968), Decision Analysis. Reading, MA: Addison-Wesley.Google Scholar