Combining stated and revealed preferences

2025-07-17

AI Summary

This paper addresses endogeneity in demand function estimation arising from unobserved individual heterogeneity. Methodologically, it proposes a causal identification framework integrating stated preferences (SP) and revealed preferences (RP). It introduces the first nonparametric model that identifies the joint distribution of unobserved heterogeneity using stated choices under hypothetical scenarios, and derives tight bounds on causal demand effects under both matched and unmatched data structures; bootstrap-based inference ensures robustness in small samples. Simulation results demonstrate consistent recovery of causal demand effects, with boundary estimators exhibiting favorable statistical properties—including consistency and valid coverage. The approach provides a falsifiable, function-form-free alternative for structural demand modeling under confounded heterogeneity, bridging theoretical rigor with empirical tractability.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Causal Learning

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

Can stated preferences inform counterfactual analyses of actual choice? This research proposes a novel approach to researchers who have access to both stated choices in hypothetical scenarios and actual choices, matched or unmatched. The key idea is to use stated choices to identify the distribution of individual unobserved heterogeneity. If this unobserved heterogeneity is the source of endogeneity, the researcher can correct for its influence in a demand function estimation using actual choices and recover causal effects. Bounds on causal effects are derived in the case, where stated choice and actual choices are observed in unmatched data sets. These data combination bounds are of independent interest. We derive a valid bootstrap inference for the bounds and show its good performance in a simulation experiment.

Problem

• Combining stated and actual choices for analysis
• Identifying unobserved heterogeneity using stated preferences
• Correcting endogeneity in demand function estimation

Innovation

• Combining stated and actual choice data
• Identifying unobserved heterogeneity distribution
• Deriving causal effect bounds