Our Seminars

Title: Market Counterfactuals with Nonparametric Supply: An ML/AI Approach (with Harold Chiang, Jack Collison, and Chris Sullivan).

Abstract: This paper develops a flexible approach to perform market counterfactuals using machine learning methods and nonparametric structure from economics. While standard structural methods rely on restrictive assumptions about firm conduct and cost, we propose a data-driven framework that relaxes these constraints when rich market data are available. Building on the identification results of Berry and Haile (2014) we develop a nonparametric model of supply that nests traditional conduct specifications while allowing for more complex competitive interactions. We adapt the Variational Method of Moments (VMM) (Bennett and Kallus, 2023) to estimate this flexible model, addressing the endogeneity of market shares and the high dimensionality of the problem. Our approach enables a wide range of counterfactual exercises including tax policy analysis, product regulation, and merger simulation. Monte Carlo simulations demonstrate that our method substantially outperforms standard approaches; applied to the American Airlines-US Airways merger, our method produces more accurate post-merger price predictions.