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Likelihood inference for discretely observed non-linear diffusions

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  • Neil Shephard
  • Ola Elerian
  • Siddhartha Chib

Abstract

This paper is concerned with the Bayesian estimation of non-linear stochastic differential equations when only discrete observations are available. The estimation is carried out using a tuned MCMC method, in particular a blocked Metropolis-Hastings algorithm, by introducing auxiliary points and using the Euler-Maruyama discretisation scheme. Techniques for computing the likelihood function, the marginal likelihood and diagnostic measures (all based on the MCMC output) are presented. Examples using simulated and real data are presented and discussed in detail.

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File URL: http://www.nuff.ox.ac.uk/economics/papers/index1998.htm
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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 1998-W10.

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Date of creation: 01 Aug 1998
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Handle: RePEc:oxf:wpaper:1998-w10

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Related research

Keywords: Bayes estimation; nonlinear diffusion; Euler-Maruyama approximation; Maximum Likelihood; Markov chain Monte Carlo; Metropolis Hastings algorithm; missing data; Simulation; Stochastic Differential Equation;

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