An accurate global algorithm is crucial for quantifying the dynamics of the Diamond-Mortensen-Pissarides model. Loglinearization understates the mean and volatility of unemployment, overstates the unemployment-vacancy correlation, and ignores impulse responses that are an order of magnitude larger in recessions than in booms. Although improving on loglinearization, the second-order perturbation in logs also induces large approximation errors. We demonstrate these insights within the context of Hagedorn and Manovskii (2008). Our quantitative results highlight the extreme importance of accurately accounting for nonlinear dynamics in quantitative macroeconomic studies.