Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288014 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 42 [Issue:] 5 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2022 [Pages:] 1069-1085
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
This paper proposes a general method to handle forecasts exposed to behavioral bias by finding appropriate outside views, in our case corporate sales forecasts of analysts. The idea is to find reference classes, that is, peer groups, for each analyzed company separately that share similarities to the firm of interest with respect to a specific predictor. The classes are regarded to be optimal if the forecasted sales distributions match the actual distributions as closely as possible. The forecast quality is measured by applying goodness‐of‐fit tests on the estimated probability integral transformations and by comparing the predicted quantiles. The method is out‐of‐sample backtested on a data set consisting of 21,808 US firms over the time period 1950–2019, which is also descriptively analyzed. It appears that, in particular, the past operating margins are good predictors for the distribution of future sales. A case study compares the outside view of our distributional forecasts with actual analysts' forecasts and emphasizes the relevance of our approach in practice.
Subjects: 
Distributional Forecast
Goodness of Fit
Outside View
Prediction
Bias Correction
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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