The Inverse Product Differentiation Logit Model

Mogens Fosgerau, Julien Monardo () and André de Palma ()
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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 - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique

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Abstract: 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.

Keywords: Demand estimation; Demand invertibility; Differentiated products; Discrete choice; Nested logit; Random utility; Representative consumer (search for similar items in EconPapers)
Date: 2019-07-15
New Economics Papers: this item is included in nep-com, nep-dcm, nep-tre and nep-upt
Note: View the original document on HAL open archive server: https://hal.science/hal-02183411v1
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Citations: View citations in EconPapers (8)

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Related works:
Journal Article: The Inverse Product Differentiation Logit Model (2024)
Working Paper: The Inverse Product Differentiation Logit Model (2022)
Working Paper: The Inverse Product Differentiation Logit Model (2021)
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