Comparing for Different Time Series Methods the Value of Technical Expertise Individualized Analysis, and Judgmental Adjustment

Robert Carbone, Allan Andersen, Yvan Corriveau and Paul Piat Corson
Additional contact information
Robert Carbone: Université Laval, Quebec, Canada
Allan Andersen: University of Sydney, Australia
Yvan Corriveau: Université Laval, Quebec, Canada
Paul Piat Corson: Université Laval, Quebec, Canada

Management Science, 1983, vol. 29, issue 5, 559-566

Abstract: Technical expertise, human judgment, and the time spent by an analyst are often believed to be key factors in determining the accuracy of forecasts obtained with the use of a time series forecasting method. A control experiment was designed to empirically test these beliefs. It involved the participation of experts and persons with limited training. Forecasts were generated for 25 time series with the use of the Box-Jenkins, Holt-Winters and Carbone-Longini filtering methods. Results of the nonparametric tests used to compare the forecasts confirmed that technical expertise, judgmental adjustment, and individualized analyses were of little value in improving forecast accuracy as compared to black box approaches. In addition, simpler methods were found to provide significantly more accurate forecasts than the Box-Jenkins method when applied by persons with limited training.

Keywords: forecasting/time; series (search for similar items in EconPapers)
Date: 1983
References: Add references at CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.29.5.559 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:29:y:1983:i:5:p:559-566

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().