Besbes, OmarPhillips, Robert2023-05-232023-05-232010-01-012016-07-15https://repository.upenn.edu/handle/20.500.14332/42048The 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.pricingparametric and nonparametric estimationmodel misspecificationhypothesis testinggoodness-of-fit testasymptotic analysisperformance analysisOther BusinessOther Medicine and Health SciencesArticle