Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares
Jean-Pierre DubÃ© (),
Ali HortaÃ§su () and
Joonhwi Joo ()
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Jean-Pierre DubÃ©: University of Chicago - Booth School of Business
Ali HortaÃ§su: University of Chicago
Joonhwi Joo: University of Texas at Dallas, Naveen Jindal School of Management, Department of Finance
No 2020-13, Working Papers from Becker Friedman Institute for Research In Economics
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.
Keywords: consumer demand; pairwise-difference estimation; endogeneity; consideration (search for similar items in EconPapers)
JEL-codes: C1 C5 C6 D11 L00 M3 (search for similar items in EconPapers)
Pages: 44 pages
New Economics Papers: this item is included in nep-dcm
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Persistent link: https://EconPapers.repec.org/RePEc:bfi:wpaper:2020-13
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