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Tuesday, February 23, 2021 at 11:15am to 12:45pmVirtual Event
Ali Hortacsu, University of Chicago
Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares, (joint w/Jean-Pierre Dubé, University of Chicago Booth School of Business & NBER, & Joonhwi Joo, University of Texas at Dallas Naveen Jindal School of Management).
Abstract: Although typically overlooked, many purchase datasets exhibit a high incidence of products with zero sales. We propose a new estimator for the Random-Coefficients Logit demand system for purchase datasets with zero-valued market shares. The identification of the demand parameters is based on a pairwise-differencing approach that constructs moment conditions based on differences in demand between pairs of products. The corresponding estimator corrects non-parametrically for the potential selection of the incidence of zeros on unobserved aspects of demand. The estimator also corrects for the potential endogeneity of marketing variables both in demand and in the selection propensities. Monte Carlo simulations show that our proposed estimator provides reliable small-sample inference both with and without selection-on-unobservables. In an empirical case study, the proposed estimator not only generates different demand estimates than approaches that ignore selection in the incidence of zero shares, it also generates better out-of-sample fit of observed retail contribution margins.
If you are interested participating in this seminar, please email Ulrike Kroeller at email@example.com for Zoom information.