Demand estimation without outside good shares

Economics > Econometrics [Submitted on 22 Feb 2026 (v1), last revised 16 Jun 2026 (this version, v2)] Title:Demand estimation without outside good shares View PDF HTML (experimental)Abstract:The BLP model is the workhorse framework for estimating demand for differentiated products using aggregate product shares. In practice, however, the share of the outside good is often unavailable. This paper studies identification and inference in the BLP model when the share of the outside good is unobserved. We show that the model is partially identified, and we derive the identified sets for the structural parameters and other quantities of economic interest. We also develop inference procedures based on moment inequalities that deliver valid confidence sets for these structural parameters and quantities of economic interest. We illustrate our results with an empirical application based on the tuna data analyzed by Gandhi et al. (2023). Submission history From: Federico Bugni [view email][v1] Sun, 22 Feb 2026 12:34:55 UTC (440 KB) [v2] Tue, 16 Jun 2026 02:56:19 UTC (416 KB) References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) 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.