Testing the validity of a demand model: An operations perspective

Omar Besbes*, Robert Phillips, Assaf Zeevi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The fields of statistics and econometrics have developed powerful methods for testing the validity (specification) of a model based on its fit to underlying data. Unlike statisticians, managers are typically more interested in the performance of a decision rather than the statistical validity of the underlying model. We propose a framework and a statistical test that incorporate decision performance into a measure of statistical validity. Under general conditions on the objective function, asymptotic behavior of our test admits a sharp and simple characterization. We develop our approach in a revenue management setting and apply the test to a data set used to optimize prices for consumer loans. We show that traditional model-based goodness-of-fit tests may consistently reject simple parametric models of consumer response (e.g., the ubiquitous logit model), while at the same time these models may "pass" the proposed performance-based test. Such situations arise when decisions derived from a postulated (and possibly incorrect) model generate results that cannot be distinguished statistically from the best achievable performance-i.e., when demand relationships are fully known. " 2010 INFORMS.

Original languageEnglish
Pages (from-to)162-183
Number of pages22
JournalManufacturing and Service Operations Management
Volume12
Issue number1
DOIs
StatePublished - Dec 2010
Externally publishedYes

Keywords

  • Asymptotic analysis
  • Goodness-of-fit test
  • Hypothesis testing
  • Model misspecification
  • Parametric and nonparametric estimation
  • Performance analysis
  • Pricing

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