Demand estimation without outside good shares

2026-02-22

AI Summary

This study addresses the challenge of identification and inference in the Berry–Levinsohn–Pakes (BLP) demand model when the outside good’s market share is unobserved. It establishes, for the first time under this setting, partial identification of the structural parameters by deriving sharp identification sets through a system of moment inequalities that incorporate equilibrium constraints. The paper further develops a method for constructing uniformly valid confidence sets for these parameters. By integrating tools from partial identification theory, moment inequality estimation, and set-valued inference, the work provides a statistically rigorous framework for demand estimation even in the absence of outside-share data, thereby offering a novel and practical approach for empirical industrial organization research.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

The BLP model is the workhorse framework in empirical IO and enables estimation of demand models for differentiated products using aggregate product shares. In practice, however, the share of the outside good is often unobserved. 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 sharp identified sets for structural parameters and equilibrium objects. We also develop inference procedures based on moment inequalities that deliver valid confidence sets for these structural parameters and equilibrium objects.

Problem

• BLP model
• demand estimation
• outside good
• partial identification
• moment inequalities

Innovation

• partial identification
• BLP model
• outside good
• moment inequalities
• sharp identified sets