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Indirect Likelihood Inference (revised)

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  • Michael Creel

    ()

  • Dennis Kristensen

Abstract

Standard indirect Inference (II) estimators take a given finite-dimensional statistic, Z_{n} , and then estimate the parameters by matching the sample statistic with the model-implied population moment. We here propose a novel estimation method that utilizes all available information contained in the distribution of Z_{n} , not just its first moment. This is done by computing the likelihood of Z_{n}, and then estimating the parameters by either maximizing the likelihood or computing the posterior mean for a given prior of the parameters. These are referred to as the maximum indirect likelihood (MIL) and Bayesian Indirect Likelihood (BIL) estimators, respectively. We show that the IL estimators are first-order equivalent to the corresponding moment-based II estimator that employs the optimal weighting matrix. However, due to higher-order features of Z_{n} , the IL estimators are higher order efficient relative to the standard II estimator. The likelihood of Z_{n} will in general be unknown and so simulated versions of IL estimators are developed. Monte Carlo results for a structural auction model and a DSGE model show that the proposed estimators indeed have attractive finite sample properties.

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Bibliographic Info

Paper provided by Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC) in its series UFAE and IAE Working Papers with number 931.13.

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Length: 32
Date of creation: 12 Jun 2013
Date of revision:
Handle: RePEc:aub:autbar:931.13

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Keywords: Approximate Bayesian Computation; Indirect Inference; maximum-likelihood; simulation-based methods.;

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  2. Ruge-Murcia, Francisco J., 2007. "Methods to estimate dynamic stochastic general equilibrium models," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2599-2636, August.
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Cited by:
  1. Martin M. Andreasen & Jesús Fernández-Villaverde & Juan Rubio-Ramírez, 2013. "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications," NBER Working Papers 18983, National Bureau of Economic Research, Inc.

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