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

Jean-Pierre H. Dubé, Ali Hortacsu and Joonhwi Joo

No 26795, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: D12 L00 L66 L81 M3 (search for similar items in EconPapers)
Date: 2020-02
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
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Published as Jean-Pierre Dubé & Ali Hortaçsu & Joonhwi Joo, 2021. "Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares," Marketing Science, vol 40(4), pages 637-660.

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