Indirect likelihood inference


Autoria(s): Creel, Michael D.; Kristensen, Dennis
Contribuinte(s)

Universitat Autònoma de Barcelona. Unitat de Fonaments de l'Anàlisi Econòmica

Institut d'Anàlisi Econòmica

Data(s)

09/06/2011

Resumo

Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect likelihood (MIL) estimator. We also propose a computationally tractable Bayesian version of the estimator which we refer to as a Bayesian Indirect Likelihood (BIL) estimator. In most cases, the density of the statistic will be of unknown form, and we develop simulated versions of the MIL and BIL estimators. We show that the indirect likelihood estimators are consistent and asymptotically normally distributed, with the same asymptotic variance as that of the corresponding efficient two-step GMM estimator based on the same statistic. However, our likelihood-based estimators, by taking into account the full finite-sample distribution of the statistic, are higher order efficient relative to GMM-type estimators. Furthermore, in many cases they enjoy a bias reduction property similar to that of the indirect inference estimator. Monte Carlo results for a number of applications including dynamic and nonlinear panel data models, a structural auction model and two DSGE models show that the proposed estimators indeed have attractive finite sample properties.

Formato

49

450681 bytes

application/pdf

Identificador

http://hdl.handle.net/2072/152049

Idioma(s)

eng

Relação

Working papers; 874.11

Direitos

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Tipo

info:eu-repo/semantics/workingPaper