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Efficient importance sampling maximum likelihood estimation of stochastic differential equations

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  • Pastorello, S.
  • Rossi, E.

Abstract

Maximum likelihood estimation (MLE) of stochastic differential equations (SDEs) is difficult because in general the transition density function of these processes is not known in closed form, and has to be approximated somehow. An approximation based on efficient importance sampling (EIS) is detailed. Monte Carlo experiments, based on widely used diffusion processes, evaluate its performance against an alternative importance sampling (IS) strategy, showing that EIS is at least equivalent, if not superior, while allowing a greater flexibility needed when examining more complicated models.

Suggested Citation

  • Pastorello, S. & Rossi, E., 2010. "Efficient importance sampling maximum likelihood estimation of stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2753-2762, November.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2753-2762
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Niu Wei-Fang, 2013. "Maximum likelihood estimation of continuous time stochastic volatility models with partially observed GARCH," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 421-438, September.
    2. Sun, Libo & Lee, Chihoon & Hoeting, Jennifer A., 2015. "A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 54-67.
    3. Höök, Lars Josef & Lindström, Erik, 2016. "Efficient computation of the quasi likelihood function for discretely observed diffusion processes," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 426-437.
    4. repec:eee:ecomod:v:222:y:2011:i:11:p:1793-1799 is not listed on IDEAS
    5. Jean-François Richard, 2015. "Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables," Working Paper 5778, Department of Economics, University of Pittsburgh.

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