We assess the performance of three hospital merger simulation methods by means of a Monte Carlo experiment. We first specify a rich theoretical model of hospital markets and use it to generate "true" price effects of a large number of hospital mergers. We then use the theoretical model to generate the data that would be available in a real-world prospective merger analysis and apply the merger simulation methods to those data. Finally, we compare the predictions of the merger simulation methods to the true price effects. We find that, while there is some heterogeneity in performance, all three simulation methods perform reasonably well. We also develop an approach to predicting the price effects of mergers that extends the analysis in Garmon (2017). That paper estimates an elasticity that can be multiplied by the percent change in WTP to generate a predicted merger price effect. In our approach we perform a similar exercise, but the elasticity is modeled as a function of pre-merger hospital gross margins. This extension captures the effect of variation in some key model parameters on the estimated elasticity.