A Bayesian Technique for Selecting a Linear Forecasting Model

Ramona L. Trader
Additional contact information
Ramona L. Trader: University of Maryland, College Park

Management Science, 1983, vol. 29, issue 5, 622-632

Abstract: The specification of a forecasting model is considered in the context of linear multiple regression. Several potential predictor variables are available, but some of them convey little information about the dependent variable which is to be predicted. A technique for selecting the "best" set of predictors which takes into account the inherent uncertainty in prediction is detailed. In addition to current data, there is often substantial expert opinion available which is relevant to the forecasting problem. The approach taken here utilizes both data and expert judgment by incorporating them into a Bayesian predictive distribution. Precise forecasting models are constructed by selecting the set of predictors which minimizes a measure of variability in prediction. An empirical demonstration of the technique is provided.

Keywords: forecasting (search for similar items in EconPapers)
Date: 1983
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.29.5.622 (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:622-632

Access Statistics for this article

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