Omar Besbes

Testing the Validity of a Demand Model: An Operations Perspective

Coauthor(s): Robert Phillips, Assaf Zeevi. View Publication

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 incorporates 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.

Source: Manufacturing & Service Operations Management
Exact Citation:
Besbes, Omar, Robert Phillips, and Assaf Zeevi. "Testing the Validity of a Demand Model: An Operations Perspective." Manufacturing & Service Operations Management 12, no. 1 (Winter 2010): 162-183.
Volume: 12
Number: 1
Pages: 162-183
Date: Winter 2010