Indirect inference through prediction

By recasting indirect inference estimation as a prediction rather than a minimization and by using regularized regressions, we can bypass the three major problems of estimation: selecting the summary statistics, defining the distance function and minimizing it numerically. By substituting regression with classification we can extend this approach to model selection as well. We present three examples: a statistical fit, the parametrization of a simple real business cycle model and heuristics selection in a fishery agent-based model. The outcome is a method that automatically chooses summary statistics, weighs them and use them to parametrize models without running any direct minimization.