Simulated Maximum Likelihood in Nonlinear Continuous-Discrete State Space Models: Importance Sampling by Approximate Smoothing

Hermann Singer
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Hermann Singer: FernUniversität Hagen

Computational Statistics, 2003, vol. 18, issue 1, No 5, 79-106

Abstract: Summary The likelihood function of a continuous-discrete state space model is computed recursively by Monte Carlo integration, using importance sampling techniques. A functional integral representation of the transition density is utilized and importance densities are obtained by smoothing. Examples are the likelihood surfaces of an AR(2) process, a Ginzburg-Landau model and stock price models with stochastic volatilities.

Keywords: Stochastic differential equations; Nonlinear filtering; Discrete noisy measurements; Maximum likelihood estimation; Monte Carlo simulation; Importance sampling (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1007/s001800300133

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