The Inverse Product Differentiation Logit Model

André de Palma (), Mogens Fosgerau and Julien Monardo ()
Additional contact information
Julien Monardo: Université de Cergy-Pontoise, THEMA

No 2021-04, THEMA Working Papers from THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise

Abstract: We propose the Inverse Product Differentiation Logit (IPDL) model, a structural (inverse) demand model for differentiated products that captures market segmentation with segments that may overlap in any way. The IPDL model generalizes the nested logit model to allow richer substitution patterns, including complementarity in demand, and can be estimated by linear instrumental variable regression using aggregate data. We use the IPDL model to estimate the demand for cereals in Chicago. We then extend it to a general demand model that is consistent with a utility model of heterogeneous, utilitymaximizing consumers.

JEL-codes: C26 D11 D12 L (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-com, nep-dcm, nep-ind, nep-ore and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://thema.u-cergy.fr/IMG/pdf/2021-04.pdf (application/pdf)

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 (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ema:worpap:2021-04

Access Statistics for this paper

More papers in THEMA Working Papers from THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise Contact information at EDIRC.
Bibliographic data for series maintained by Stefania Marcassa ( this e-mail address is bad, please contact ).