Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares

Jean-Pierre Dubé (), Ali Hortaçsu () and Joonhwi Joo ()
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Jean-Pierre Dubé: University of Chicago Booth School of Business, Chicago, Illinois 60637; National Bureau of Economic Research, Cambridge, Massachusetts 02138
Ali Hortaçsu: National Bureau of Economic Research, Cambridge, Massachusetts 02138; Kenneth C. Griffin Department of Economics, University of Chicago, Chicago, Illinois 60637
Joonhwi Joo: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080

Marketing Science, 2021, vol. 40, issue 4, 637-660

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 nonparametrically 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.

Keywords: demand estimation; selection correction (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:40:y:2021:i:4:p:637-660

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