Antitrust, Consumer credit, Consumer protection, FTC, Hospital mergers, Pharmaceuticals,

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We perform a Monte Carlo experiment to assess the performance of three hospital merger simulation methods. Our analysis proceeds as follows: (i) specify a theoretical model of hospital markets and use it to generate "true" price effects for many simulated mergers; (ii) for each simulated merger, generate data of the kind commonly available in real-world merger analysis and apply the simulation methods to those data; and (iii) compare the predictions of the simulation methods to the true price effects. All three simulation methods perform reasonably well. We also develop a method for predicting price effects that extends Garmon [2017]. I. INTRODUCTION IN RECENT YEARS, THE ECONOMICS LITERATURE HAS PRODUCED A NUMBER OFMETHODS for predicting the price effects of hospital mergers. We refer to these as "simulation" methods. These methods are appealing because they are reasonably tractable and can be applied to data that are commonly available in real-world merger cases. The methods have been used in internal analyses at the Federal Trade Commission (Farrell et al. [2011]), and also by testifying economic experts in recent litigated hospital merger cases. 1 The critical question about these simulation methods is how well they perform in predicting real-world price effects of mergers. An important recent *We are grateful to