This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Demand Forecasting: Evidence-based Methods

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
J. Scott Armstrong
Kesten C. Green

Additional information is available for the following registered author(s):

Abstract

We looked at evidence from comparative empirical studies to identify methods that can be useful for predicting demand in various situations and to warn against methods that should not be used. In general, use structured methods and avoid intuition, unstructured meetings, focus groups, and data mining. In situations where there are sufficient data, use quantitative methods including extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Otherwise, use methods that structure judgement including surveys of intentions and expectations, judgmental bootstrapping, structured analogies, and simulated interaction. Managers' domain knowledge should be incorporated into statistical forecasts. Methods for combining forecasts, including Delphi and prediction markets, improve accuracy. We provide guidelines for the effective use of forecasts, including such procedures as scenarios. Few organizations use many of the methods described in this paper. Thus, there are opportunities to improve efficiency by adopting these forecasting practices.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2005/wp24-05.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 24/05.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 17 pages
Date of creation: Sep 2005
Date of revision:
Handle: RePEc:msh:ebswps:2005-24

Contact details of provider:
Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Phone: +61-3-9905-2489
Fax: +61-3-9905-5474
Email:
Web page: http://www.buseco.monash.edu.au/depts/ebs/
More information through EDIRC

Order Information:
Email:
Web: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/

For technical questions regarding this item, or to correct its listing, contact: (Simone Grose).

Related research
Keywords: Accuracy; expertise; forecasting; judgement; marketing.;

Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
M30 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - General
M31 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Marketing

This paper has been announced in the following NEP Reports:

This item is featured on the following reading lists:
  1. Technology Assessment
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Armstrong, J. Scott & Morwitz, Vicki G. & Kumar, V., 2000. "Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy?," International Journal of Forecasting, Elsevier, vol. 16(3), pages 383-397. [Downloadable!] (restricted)
  2. Dangerfield, Byron J. & Morris, John S., 1992. "Top-down or bottom-up: Aggregate versus disaggregate extrapolations," International Journal of Forecasting, Elsevier, vol. 8(2), pages 233-241, October. [Downloadable!] (restricted)
  3. Armstrong, J Scott & Collopy, Fred, 2001. "Identification of Asymmetric Prediction Intervals through Causal Forces," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 273-83, July.
  4. Kesten C. Green & J. Scott Armstrong, 2004. "Structured analogies for forecasting," Monash Econometrics and Business Statistics Working Papers 17/04, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
    Other versions:
  5. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring. [Downloadable!] (restricted)
    Other versions:
  6. Green, Kesten C., 2002. "Forecasting decisions in conflict situations: a comparison of game theory, role-playing, and unaided judgement," International Journal of Forecasting, Elsevier, vol. 18(3), pages 321-344. [Downloadable!] (restricted)
  7. Green, Kesten C., 2005. "Game theory, simulated interaction, and unaided judgement for forecasting decisions in conflicts: Further evidence," International Journal of Forecasting, Elsevier, vol. 21(3), pages 463-472. [Downloadable!] (restricted)
  8. Kesten C. Green & J. Scott Armstrong, 2004. "Value of Expertise For Forecasting Decisions in Conflicts," Monash Econometrics and Business Statistics Working Papers 27/04, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  9. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June. [Downloadable!] (restricted)
  10. Paul W. Rhode & Koleman S. Strumpf, 2004. "Historical Presidential Betting Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 127-142, Spring. [Downloadable!] (restricted)
  11. Makridakis, Spyros & Hibon, Michele & Lusk, Ed & Belhadjali, Moncef, 1987. "Confidence intervals: An empirical investigation of the series in the M-competition," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 489-508. [Downloadable!] (restricted)
  12. JS Armstrong & Fred Collopy, 2004. "Integration of Statistical Methods and Judgment for Time Series," General Economics and Teaching 0412024, EconWPA. [Downloadable!]
  13. Tyebjee, Tyzoon T., 1987. "Behavioral biases in new product forecasting," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 393-404. [Downloadable!] (restricted)
Full references

Statistics
Access and download statistics

Did you know? The yearly budget of IDEAS is exactly $0: it relies entirely on volunteer work.

This page was last updated on 2009-10-21.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.