  Indirect Inference (I-I) is a popular technique for estimating complex
parametric models whose likelihood function is intractable, however, the
statistical efficiency of I-I estimation is questionable. While the efficient
method of moments, Gallant and Tauchen (1996), promises efficiency, the price
to pay for this efficiency is a loss of parsimony and thereby a potential lack
of robustness to model misspecification. This stands in contrast to simpler I-I
estimation strategies, which are known to display less sensitivity to model
misspecification precisely due to their focus on specific elements of the
underlying structural model. In this research, we propose a new
simulation-based approach that maintains the parsimony of I-I estimation, which
is often critical in empirical applications, but can also deliver estimators
that are nearly as efficient as maximum likelihood. This new approach is based
on using a constrained approximation to the structural model, which ensures
identification and can deliver estimators that are nearly efficient. We
demonstrate this approach through several examples, and show that this approach
can deliver estimators that are nearly as efficient as maximum likelihood, when
feasible, but can be employed in many situations where maximum likelihood is
infeasible.
