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Finite Sample Properties of the Efficient Method of Moments

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  • Romulo Chumacero

    (University of Chile)

Abstract
Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method of Moments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique uses as matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subset of variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first one compares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average (MA) process. The second and third experiments compare the finite sample properties of the EMM estimators with those of GMM by using stochastic volatility models and consumption-based asset-pricing models. The experiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share the same type of objective function, finite sample inference based on asymptotic theory continues to lead, in some cases, to "over rejections," even though they are not as significant as in GMM.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Romulo Chumacero, "undated". "Finite Sample Properties of the Efficient Method of Moments," Computing in Economics and Finance 1997 5, Society for Computational Economics.
    • Handle: RePEc:sce:scecf7:5
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    Cited by:

    1. Chumacero Rómulo A., 2001. "Estimating ARMA Models Efficiently," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(2), pages 1-14, July.
    2. Gallant, A. Ronald & Tauchen, George, 2002. "Simulated Score Methods and Indirect Inference for Continuous-time Models," Working Papers 02-09, Duke University, Department of Economics.
    3. Hao Zhou, 2000. "A study of the finite sample properties of EMM, GMM, QMLE, and MLE for a square-root interest rate diffusion model," Finance and Economics Discussion Series 2000-45, Board of Governors of the Federal Reserve System (U.S.).
    4. Coppejans, Mark & Gallant, A. Ronald, 2002. "Cross-validated SNP density estimates," Journal of Econometrics, Elsevier, vol. 110(1), pages 27-65, September.
    5. Rómulo A. Chumacero, 2005. "A Toolkit for Analyzing Alternative Policies in the Chilean Economy," Central Banking, Analysis, and Economic Policies Book Series, in: Rómulo A. Chumacero & Klaus Schmidt-Hebbel & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (S (ed.),General Equilibrium Models for the Chilean Economy, edition 1, volume 9, chapter 8, pages 261-302, Central Bank of Chile.
    6. Veiga, Helena, 2006. "A two factor long memory stochastic volatility model," DES - Working Papers. Statistics and Econometrics. WS ws061303, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Michael Creel, 2008. "Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments," UFAE and IAE Working Papers 725.08, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC), revised 02 Jun 2008.
    8. Ronald Gallant, A. & Tauchen, George, 1999. "The relative efficiency of method of moments estimators1," Journal of Econometrics, Elsevier, vol. 92(1), pages 149-172, September.
    9. Monica Gentile & Roberto Renò, 2002. "Which Model for the Italian Interest Rates?," LEM Papers Series 2002/02, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    10. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2013. "Indirect Inference in fractional short-term interest rate diffusions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 109-126.
    11. Romulo A. Chumacero, 1999. "Estimating Stationary ARMA Models Efficiently," Computing in Economics and Finance 1999 1333, Society for Computational Economics.

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