  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.
