Technology Category
Reasoning under Uncertainty: Other Foundations of Reasoning under UncertaintyGame Theory and Economic Paradigms: Mechanism DesignMultiagent Systems: Mechanism Design
Demand estimation without outside good shares
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
BLP model
demand estimation
outside good
partial identification
moment inequalities
Innovation
Methods, ideas, or system contributions that make the work stand out.
partial identification
BLP model
outside good
moment inequalities
sharp identified sets
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