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Indirect likelihood inference (2011).
| Content Provider | CiteSeerX |
|---|---|
| Author | Creel, Michael Kristensen, Dennis |
| Abstract | 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 twostep 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. |
| File Format | |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Indirect Likelihood Inference Sample Moment Indirect Likelihood Estimator Full Finite-sample Distribution Bias Reduction Property Tractable Bayesian Version Many Case Nonlinear Panel Data Model Simulated Version Indirect Inference Estimator Parametric Model Maximum Indirect Likelihood Structural Auction Model Attractive Finite Sample Property Gmm-type Estimator Likelihood-based Estimator Corresponding Efficient Twostep Gmm Estimator Monte Carlo Result Asymptotic Variance Bil Estimator Dsge Model Bayesian Indirect Likelihood Unknown Form Finite-dimensional Statistic Initial Estimator |
| Content Type | Text |
| Resource Type | Article |
