Indirect Likelihood Inference
Dennis Kristensen and
Michael Creel
No 558, Working Papers from Barcelona School of Economics
Abstract:
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.
Keywords: Bayesian estimation; indirect inference; maximum-likelihood; simulation-based methods; bias correction (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 C33 (search for similar items in EconPapers)
Date: 2015-09
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Citations: View citations in EconPapers (17)
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Related works:
Working Paper: Indirect likelihood inference (2011) 
Working Paper: Indirect Likelihood Inference (2011) 
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Persistent link: https://EconPapers.repec.org/RePEc:bge:wpaper:558
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