“…Faster and more capable computing spurred the development of methods to estimate parameters in nonlinear systems, avoid getting stuck on local optima in parameter space, deal with autocorrelation and heteroscedasticity, and especially model misspecification and causal identification (see below). These methods include indirect inference (Gourieroux et al, ; Hosseinichimeh et al, ), the simulated method of moments (McFadden, ; Jalali et al, ), Generalized Model Aggregation (Rahmandad et al, ) and extended Kalman and particle filters (Fernández‐Villaverde and Rubio‐Ramírez, ). Bootstrapping and subsampling (see, e.g., Politis and Romano, ; Dogan, ; Struben et al, ) and Markov chain Monte Carlo and related Bayesian methods (Ter Braak, ; Vrugt et al, ; Osgood and Liu, ) became feasible for models of realistic size and complexity, enabling modelers to estimate parameters and the confidence intervals around them without making unrealistic assumptions about the properties of the models and error terms.…”