The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Principles of Forecasting: A Handbook for Researchers and Practitioners
Open accessAbstract
Principles of Forecasting: A Handbook for Researchers and Practitioners summarizes knowledge from experts and from empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. It applies to problems such as those in finance (How much is this company worth?), marketing (Will a new product be successful?), personnel (How can we identify the best job candidates?), and production (What level of inventories should be kept?). The book is edited by Professor J. Scott Armstrong of the Wharton School, University of Pennsylvania. Contributions were written by 40 leading experts in forecasting, and the 30 chapters cover all types of forecasting methods. There are judgmental methods such as Delphi, role-playing, and intentions studies. Quantitative methods include econometric methods, expert systems, and extrapolation. Some methods, such as conjoint analysis, analogies, and rule-based forecasting, integrate quantitative and judgmental procedures. In each area, the authors identify what is known in the form of `if-then principles', and they summarize evidence on these principles. The project, developed over a four-year period, represents the first book to summarize all that is known about forecasting and to present it so that it can be used by researchers and practitioners. To ensure that the principles are correct, the authors reviewed one another's papers. In addition, external reviews were provided by more than 120 experts, some of whom reviewed many of the papers. The book includes the first comprehensive forecasting dictionary.
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Cites
Construct validity in psychological tests.
Cronbach, L. J., & Meehl, P. E. (1955) · Psychological Bulletin · doi:10.1037/h0040957 · Cited by 11,195
Cohen, J. (1994) · American Psychologist · doi:10.1037/0003-066x.49.12.997 · Cited by 4,003
Bates, J. M., & Granger, C. W. J. (1969) · Journal of the Operational Research Society · doi:10.1057/jors.1969.103 · Cited by 3,201
The Impact of Empirical Accuracy Studies on Time Series Analysis and Forecasting
Fildes, R., & Makridakis, S. (1995) · International Statistical Review / Revue Internationale de Statistique · doi:10.2307/1403481 · Cited by 172
Cited by
Another look at measures of forecast accuracy
Hyndman, R. J., & Koehler, A. B. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.03.001 · Cited by 5,757
Conditions for intuitive expertise: A failure to disagree.
Kahneman, D., & Klein, G. (2009) · American Psychologist · doi:10.1037/a0016755 · Cited by 2,443
Consensus measurement in Delphi studies
von der Gracht, H. A. (2012) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2012.04.013 · Cited by 1,831
Bonaccio, S., & Dalal, R. S. (2006) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.07.001 · Cited by 1,011
Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.
Pleskac, T. J., & Busemeyer, J. R. (2010) · Psychological Review · doi:10.1037/a0019737 · Cited by 722
Metadata
- Venue
- International Series in Operations Research & Management Science
- Publisher
- Springer US
- Published
- 2001
- ISSN
- 0884-8289
- OpenAlex
- W2147042673
- Cited by
- 1,041
- References
- 4
- FWCI
- 43.74
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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