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The Inverse Product Differentiation Logit Model
Mogens Fosgerau
() (DTU - Technical University of Denmark [Lyngby])
Julien Monardo
() (ENS Cachan - École normale supérieure - Cachan, CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique)
André de Palma
(X-DEP-ECO - Département d'Économie de l'École Polytechnique - X - École polytechnique)
André Jean-Louis de Palma
This paper proposes an empirical model of inverse demand for differentiated products: the Inverse Product Differentiation Logit (IPDL) model. The IPDL model generalizes the commonly used nested logit model to allow richer substitution patterns, including complementarity. Nevertheless, the IDPL model can be estimated by two-stage least squares using aggregate data. We apply the IDPL model to data on ready-to-eat cereals in Chicago in 1991-1992, and find that complementarity is pervasive in this market. We then show that the IPDL model belongs to a wider class of inverse demand models in which products can be complements, and which is sufficiently large to encompass a large class of discrete choice demand models. We establish invertibility for this wider class, thus extending previous results on demand inversion.
Mogens Fosgerau & Julien Monardo & André de Palma, 2019. "The Inverse Product Differentiation Logit Model," Working Papers hal-02183411, HAL.
Handle: RePEc:hal:wpaper:hal-02183411
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Full references (including those not matched with items on IDEAS)
Demand estimation; Demand invertibility; Differentiated products; Discrete choice; Nested logit; Random utility; Representative consumer;
NEP fields
This paper has been announced in the following NEP Reports:
NEP-COM-2019-08-19 (Industrial Competition)
NEP-DCM-2019-08-19 (Discrete Choice Models)
NEP-TRE-2019-08-19 (Transport Economics)
NEP-UPT-2019-08-19 (Utility Models & Prospect Theory)
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