Barigozzi, Matteo
[VerfasserIn]
;
Halbleib, Roxana
[Sonstige Person, Familie und Körperschaft];
Veredas, David
[Sonstige Person, Familie und Körperschaft]
Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 11, 2015 erstellt
Beschreibung:
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