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Simulated Score Methods and Indirect Inference for Continuous-time Models

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  • Gallant, A. Ronald
  • Tauchen, George

Abstract

We describe a simulated method of moments estimator that is implemented by choosing the vector valued moment function to be the expectation under the structural model of the score function of an auxiliary model, where the parameters of the auxiliary model are eliminated by replacing them with their quasi-maximum likelihood estimates. This leaves a moment vector depending only the parameters of the structural model. Structural parameter estimates are those parameter values that put the moment vector as closely to zero as possible in a suitable GMM metric. This methodology can also be interpreted as a practical computational strategy for implementing indirect inference. We argue that considerations from statistical science dictate that the auxiliary model should approximate the true data generating process as closely as possible and show that using the SNP model is one means to that end. When the view of close approximation is accepted in implementation, the methodology described is usually referred to as Efficient Method of Moments (EMM) in the literature because (i) the estimator is asymptotically as efficient as maximum likelihood under correct specification, and (ii) detection of model error is assured under incorrect specification. There are alternative views toward the desirability of close approximation to the data, which we discuss.

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Bibliographic Info

Paper provided by Duke University, Department of Economics in its series Working Papers with number 02-09.

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Date of creation: 2002
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Handle: RePEc:duk:dukeec:02-09

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Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://econ.duke.edu/

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Cited by:
  1. 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.
  2. Jon Danielsson & Francisco Penaranda, 2007. "On the impact of fundamentals, liquidity and coordination on market stability," LSE Research Online Documents on Economics 24480, London School of Economics and Political Science, LSE Library.
  3. Marcel Rindisbacher & Jérôme Detemple & René Garcia, 2004. "Asymptotic Properties of Monte Carlo Estimators of Diffusion Processes," Econometric Society 2004 North American Winter Meetings 483, Econometric Society.
  4. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2004. "Analytical Evaluation Of Volatility Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(4), pages 1079-1110, November.
  5. Ravi Bansal & A. Ronald Gallant & George Tauchen, 2007. "Rational Pessimism, Rational Exuberance, and Asset Pricing Models," Review of Economic Studies, Oxford University Press, vol. 74(4), pages 1005-1033.
  6. Manuel Santos, 2007. "Consistency Properties of a Simulation-Based Estimator for Dynamic Processes," Working Papers 0705, University of Miami, Department of Economics.
  7. In Kim & In-Seok Baek & Jaesun Noh & Sol Kim, 2007. "The role of stochastic volatility and return jumps: reproducing volatility and higher moments in the KOSPI 200 returns dynamics," Review of Quantitative Finance and Accounting, Springer, vol. 29(1), pages 69-110, July.

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