Profit Estimation Error in the Newsvendor Model Under a Parametric Demand Distribution

Andrew F. Siegel () and Michael R. Wagner ()
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Andrew F. Siegel: Information Systems and Operations Management, Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195; Finance and Business Economics, Department of Statistics, University of Washington, Seattle, Washington 98195
Michael R. Wagner: Information Systems and Operations Management, Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195

Management Science, 2021, vol. 67, issue 8, 4863-4879

Abstract: We consider the newsvendor model in which uncertain demand is assumed to follow a probabilistic distribution with known functional form but unknown parameters. These parameters are estimated, unbiasedly and consistently, from data. We show that the classic maximized expected profit expression exhibits a systematic expected estimation error. We provide an asymptotic adjustment so that the estimate of maximized expected profit is unbiased. We also study expected estimation error in the optimal order quantity, which depends on the distribution: (1) if demand is exponentially or normally distributed, the order quantity has zero expected estimation error; (2) if demand is log-normally distributed, there is a nonzero expected estimation error in the order quantity that can be corrected. Numerical experiments, for light- and heavy-tailed distributions, confirm our theoretical results.

Keywords: newsvendor; estimation error; statistics; Fisher information (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:67:y:2021:i:8:p:4863-4879

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