Nonparametric econometrics
Models of the Spiral-Down Effect in Revenue Management
Operations Research
Manufacturing & Service Operations Management
A practical inventory control policy using operational statistics
Operations Research Letters
Monotone Approximation of Decision Problems
Operations Research
On the Minimax Complexity of Pricing in a Changing Environment
Operations Research
Now Playing: DVD Purchasing for a Multilocation Rental Firm
Manufacturing & Service Operations Management
Manufacturing & Service Operations Management
Markdown Pricing with Unknown Fraction of Strategic Customers
Manufacturing & Service Operations Management
Clearance Pricing Optimization for a Fast-Fashion Retailer
Operations Research
Clearance Pricing Optimization for a Fast-Fashion Retailer
Operations Research
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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.