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Which Model to Match?

Matteo BarigozziRoxana HalbleibDavid Veredas

出版	SSRN, 2014
URL	http://books.google.com.hk/books?id=7oEpuwEACAAJ&hl=&source=gbs_api

註釋

The asymptotic efficiency of indirect estimation methods, such as Indirect Inference and the Efficient Method of Moments, depends on the choice of the auxiliary model, which is some- how ad hoc and based on an educated guess. We introduce a consistent simulation based Akaike-type class of information criteria that helps the user in this choice among nested and non-nested auxiliary models by selecting the model that provides the best trade-off between efficiency and estimation error. We prove consistency of the proposed criterion as the sample sizes of the observed and simulated data increase. In a Monte Carlo exercise and an empirical illustration we show the usefulness of the method.