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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.

Suggested Citation

  • Carrasco, Marine & Florens, Jean-Pierre, 2002. "Simulation-Based Method of Moments and Efficiency," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 482-492, October.
  • Handle: RePEc:bes:jnlbes:v:20:y:2002:i:4:p:482-92
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    Citations

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    Cited by:

    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2007. "Estimating Macroeconomic Models: A Likelihood Approach," Review of Economic Studies, Oxford University Press, vol. 74(4), pages 1059-1087.
    2. Carrasco, Marine & Florens, Jean-Pierre, 2014. "On The Asymptotic Efficiency Of Gmm," Econometric Theory, Cambridge University Press, vol. 30(02), pages 372-406, April.
    3. Sickles, Robin C. & Williams, Jenny, 2008. "Turning from crime: A dynamic perspective," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 158-173, July.
    4. Zhenxi, Chen & Lux, Thomas, 2015. "Estimation of sentiment effects in financial markets: A simulated method of moments approach," FinMaP-Working Papers 37, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    5. 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.
    6. Erwann SbaÏ & Olivier Armantier, 2006. "Estimation and comparison of treasury auction formats when bidders are asymmetric," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 745-779.
    7. Eckstein, Zvi & Ge, Suqin & Petrongolo, Barbara, 2006. "Job and wage mobility in a search model with non-compliance (exemptions) with the minimum wage," LSE Research Online Documents on Economics 4961, London School of Economics and Political Science, LSE Library.
    8. Manuel Santos, 2007. "Consistency Properties of a Simulation-Based Estimator for Dynamic Processes," Working Papers 0705, University of Miami, Department of Economics.
    9. 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.
    10. Shintaro Yamaguchi, 2010. "Job Search, Bargaining, and Wage Dynamics," Journal of Labor Economics, University of Chicago Press, vol. 28(3), pages 595-631, July.
    11. 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.
    12. Andreasen, Martin M. & Christensen, Bent Jesper, 2015. "The SR approach: A new estimation procedure for non-linear and non-Gaussian dynamic term structure models," Journal of Econometrics, Elsevier, vol. 184(2), pages 420-451.
    13. Tae-Seok Jang, 2015. "Identification of Social Interaction Effects in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 207-238, February.
    14. 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.
    15. Anindya Biswas & Biswajit Mandal, 2016. "Estimating Preference Parameters From Stock Returns Using Simulated Method Of Moments," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 1-13, March.
    16. Carrasco, Marine & Florens, Jean-Pierre, 2011. "A Spectral Method For Deconvolving A Density," Econometric Theory, Cambridge University Press, vol. 27(03), pages 546-581, June.
    17. repec:eee:econom:v:203:y:2018:i:2:p:344-358 is not listed on IDEAS

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