Enhancing the Merger Simulation Toolkit with ML/AI

TL;DR

This paper introduces a machine learning-based framework for predicting merger price effects that relaxes traditional assumptions, improves accuracy, and enhances economic interpretability in merger analysis.

Contribution

It develops a flexible nonparametric supply model estimated via VMM, outperforming traditional methods in predictive accuracy and economic insights.

Findings

Outperforms traditional models in simulations

Provides more accurate post-merger price predictions

Learns economically meaningful markup and cost functions

Abstract

This paper develops a flexible approach to predict the price effects of horizontal mergers using ML/AI methods. While standard merger simulation techniques rely on restrictive assumptions about firm conduct, we propose a data-driven framework that relaxes these constraints when rich market data are available. We develop and identify a flexible nonparametric model of supply that nests a broad range of conduct models and cost functions. To overcome the curse of dimensionality, we adapt the Variational Method of Moments (VMM) (Bennett and Kallus, 2023) to estimate the model, allowing for various forms of strategic interaction. Monte Carlo simulations show that our method significantly outperforms an array of misspecified models and rivals the performance of the true model, both in predictive performance and counterfactual merger simulations. As a way to interpret the economics of the estimated function, we simulate pass-through and reveal that the model learns markup and cost functions that imply approximately correct pass-through behavior. Applied to the American Airlines-US Airways merger, our method produces more accurate post-merger price predictions than traditional approaches. The results demonstrate the potential for machine learning techniques to enhance merger analysis while maintaining economic structure.