Assessing Uncertainty from Point Forecasts

33 Pages Posted: 11 Jul 2016 Last revised: 10 Aug 2017

See all articles by Anil Gaba

Anil Gaba

INSEAD – Decision Sciences

Dana Popescu

INSEAD - Technology and Operations Management

Zhi Chen


Date Written: August 10, 2017


The paper develops a model for combining point forecasts into a predictive distribution for a variable of interest. Our approach allows for point forecasts to be correlated and admits uncertainty on the distribution parameters given the forecasts. Further, it provides an easy way to compute an augmentation factor needed to equate the dispersion of the point forecasts to that of the predictive distribution, which depends on the correlation between the point forecasts and on the number of forecasts. We show that ignoring dependence or parameter uncertainty can lead to assuming an unrealistically narrow predictive distribution. We further illustrate the implications in a newsvendor context, where our model leads to an order quantity that has higher variance but is biased in the less costly direction, and generates an increase in expected profit relative to other methods.

Keywords: Correlated Experts, Point Forecasts, Demand Forecasting, Newsvendor Model

Suggested Citation

Gaba, Anil and Popescu, Dana and Chen, Zhi, Assessing Uncertainty from Point Forecasts (August 10, 2017). INSEAD Working Paper No. 2017/49/DSC. Available at SSRN: or

Anil Gaba (Contact Author)

INSEAD – Decision Sciences ( email )

1 Ayer Rajah Avenue
Singapore, Select 138676


Dana Popescu

INSEAD - Technology and Operations Management ( email )

Boulevard de Constance
77 305 Fontainebleau Cedex

Zhi Chen

INSEAD ( email )

Boulevard de Constance
77305 Fontainebleau Cedex

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