Sensitivity of Weights in Combining Forecasts

In the combination of forecasts, weighted averages that attempt to take into account the accuracy of the forecasts and any dependence among forecasts tend to perform poorly in practice. An important factor influencing this performance is the sensitivity, or instability, of the estimated weights used...

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Bibliographic Details
Published inOperations research Vol. 40; no. 3; pp. 609 - 614
Main Authors Winkler, Robert L, Clemen, Robert T
Format Journal Article
LanguageEnglish
Published Linthicum, MD INFORMS 01.05.1992
Operations Research Society of America
Institute for Operations Research and the Management Sciences
Subjects
Analytical forecasting
Applied sciences
Covariance matrices
Decision making
Exact sciences and technology
Forecasting models
forecasting: combining forecasts
Forecasts
Mathematical models
Operational research and scientific management
Operational research. Management science
Operations research
Parametric models
Planning. Forecasting
Sampling distributions
Standard deviation
Statistical discrepancies
Statistical forecasts
Technical Notes
Time series forecasting
Weighted averages
Sensitivity analysis
Graph
Weighting
Forecasting
Sampling distribution
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Summary:In the combination of forecasts, weighted averages that attempt to take into account the accuracy of the forecasts and any dependence among forecasts tend to perform poorly in practice. An important factor influencing this performance is the sensitivity, or instability, of the estimated weights used to generate the combined forecast. The intent of this paper is to look at this instability via graphs and the sampling distribution of the weights. Results are developed for the combination of two forecasts and extended to the m -forecast case by viewing the m -forecast case as a sequence of two-forecast combinations.
ISSN:0030-364X
1526-5463
DOI:10.1287/opre.40.3.609