Identification and Estimation of Demand Models with Endogenous Product Entry and Exit

[2308.14196] Identification and Estimation of Demand Models with Endogenous Product Entry and Exit Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Economics > Econometrics arXiv:2308.14196 (econ) [Submitted on 27 Aug 2023 ( v1 ), last revised 28 Jul 2026 (this version, v3)] Title: Identification and Estimation of Demand Models with Endogenous Product Entry and Exit Authors: Victor Aguirregabiria , Alessandro Iaria , Senay Sokullu View a PDF of the paper titled Identification and Estimation of Demand Models with Endogenous Product Entry and Exit, by Victor Aguirregabiria and 2 other authors View PDF HTML (experimental) Abstract: Firms introduce products in markets where they anticipate stronger demand, using information unobserved by researchers. This creates endogenous selection in demand estimation. In differentiated-product oligopolies, multidimensional demand unobservables and strategic entry can render the ordinary propensity score insufficient for selection correction. Existing approaches either restrict firms' information at entry or jointly estimate demand, pricing, and entry under strong supply-side assumptions. We derive a new mixture representation of the selection-bias function using latent propensity scores: entry probabilities conditional on observables and a latent market state generating dependence across entry decisions. This representation yields a convenient two-step semiparametric estimator that corrects for selection and price endogeneity while accommodating richer information at entry. The approach makes weaker supply-side assumptions and is simpler to implement because it avoids repeatedly solving the full model. Applied to the US airline industry, the method yields more elastic demand and less market power than estimates ignoring endogenous entry. Comments: 70 pages, 3 figures, 12 tables Subjects: Econometrics (econ.EM) Cite as: arXiv:2308.14196 [econ.EM] (or arXiv:2308.14196v3 [econ.EM] for this version) https://doi.org/10.48550/arXiv.2308.14196 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Victor Aguirregabiria [ view email ] [v1] Sun, 27 Aug 2023 20:13:44 UTC (126 KB) [v2] Thu, 9 Apr 2026 18:42:27 UTC (161 KB) [v3] Tue, 28 Jul 2026 13:16:35 UTC (279 KB) Full-text links: Access Paper: View a PDF of the paper titled Identification and Estimation of Demand Models with Endogenous Product Entry and Exit, by Victor Aguirregabiria and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: econ.EM < prev | next > new | recent | 2023-08 Change to browse by: econ References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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