Series: Working Papers. 1229.
Author: Matteo Barigozzi, Roxana Halbleib and David Veredas.
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Abstract
The asymptotic efficiency of indirect estimation methods, such as the efficient method of
moments and indirect inference, depends on the choice of the auxiliary model. To date,
this choice has been somewhat ad hoc and based on an educated guess. In this article we
introduce a class of information criteria that helps the user to optimize the choice between
nested and non–nested auxiliary models. They are the indirect analogues of the widely used
Akaike–type criteria. A thorough Monte Carlo study based on two simple and illustrative
models shows the usefulness of the criteria.