Working Paper: NBER ID: w26795
Authors: Jean-Pierre H. Dub; Ali Hortasu; Joonhwi Joo
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.
Keywords: demand estimation; random coefficients logit; zero market shares; selection bias
JEL Codes: D12; L00; L66; L81; M3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| incidence of zero market shares (L17) | underlying demand factors (J23) |
| selection process (C52) | demand estimates (C51) |
| estimator (C51) | predictive accuracy (C52) |
| selection bias (C24) | demand estimation (C51) |
| endogeneity of marketing variables (M30) | demand estimation (C51) |
| first set of instruments (Y20) | price endogeneity (E30) |
| second set of instruments (Y20) | product consideration (L15) |
| three-step estimation procedure (C51) | valid causal estimates (C20) |