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Simulation-Based Method of Moments and Efficiency

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  • Carrasco, Marine
  • Florens, Jean-Pierre

Abstract

The method of moments is based on a relation E[superscript theta[subscript 0]](h(X[subscript t, theta)) = 0, from which an estimator of theta is deduced. In many econometric models, the moment restrictions can not be evaluated numerically due to, for instance, the presence of a latent variable. Monte Carlo simulations method make possible the evaluation of the generalized method of moments (GMM) criterion. This is the basis for the simulated method of moments. Another approach involves defining an auxiliary model and finding the value of the parameters that minimizes a criterion based either on the pseudoscore (efficient method of moments) or the difference between the pseudotrue value and the quasi-maximum likelihood estimator (indirect inference). If the auxiliary model is sufficiently rich to encompass the true model, then these two methods deliver an estimator that is asymptotically as efficient as the maximum likelihood estimator.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 20 (2002)
Issue (Month): 4 (October)
Pages: 482-92

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Handle: RePEc:bes:jnlbes:v:20:y:2002:i:4:p:482-92

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Cited by:
  1. Olivier Armantier & Erwann Sbai, 2003. "Estimation and Comparison of Treasury Auction Formats when Bidders are Asymmetric," Department of Economics Working Papers 03-02, Stony Brook University, Department of Economics.
  2. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2006. "Estimating Macroeconomic Models: A Likelihood Approach," NBER Technical Working Papers 0321, National Bureau of Economic Research, Inc.
  3. Shintaro Yamaguchi, 2006. "Job Search, Bargaining, and Wage Dynamics," 2006 Meeting Papers 54, Society for Economic Dynamics.
  4. Jean-Pierre Florens & Marine Carrasco, 2004. "On the Asymptotic Efficiency of GMM," Econometric Society 2004 North American Winter Meetings 436, Econometric Society.
  5. Manuel Santos, 2007. "Consistency Properties of a Simulation-Based Estimator for Dynamic Processes," Working Papers 0705, University of Miami, Department of Economics.
  6. Franke, Reiner & Jang, Tae-Seok & Sacht, Stephen, 2011. "Moment matching versus Bayesian estimation: Backward-looking behaviour in the new-Keynesian three-equations model," Economics Working Papers 2011,10, Christian-Albrechts-University of Kiel, Department of Economics.
  7. Andreasen, Martin & Meldrum, Andrew, 2013. "Likelihood inference in non-linear term structure models: the importance of the lower bound," Bank of England working papers 481, Bank of England.
  8. Franke, Reiner, 2009. "Applying the method of simulated moments to estimate a small agent-based asset pricing model," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 804-815, December.
  9. Eckstein, Zvi & Ge, Suqin & Petrongolo, Barbara, 2006. "Job and Wage Mobility in a Search Model with Non-Compliance (Exemptions) with the Minimum Wage," IZA Discussion Papers 2076, Institute for the Study of Labor (IZA).
  10. Kamhon Kan & Chihwa Kao, 2005. "Simulation-Based Two-Step Estimation with Endogenous Regressors," Center for Policy Research Working Papers 76, Center for Policy Research, Maxwell School, Syracuse University.
  11. Sickles, Robin C. & Williams, Jenny, 2008. "Turning from crime: A dynamic perspective," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 158-173, July.

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