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Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion)

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  • Alexandros Beskos
  • Omiros Papaspiliopoulos
  • Gareth O. Roberts
  • Paul Fearnhead

Abstract

Summary. The objective of the paper is to present a novel methodology for likelihood‐based inference for discretely observed diffusions. We propose Monte Carlo methods, which build on recent advances on the exact simulation of diffusions, for performing maximum likelihood and Bayesian estimation.

Suggested Citation

  • Alexandros Beskos & Omiros Papaspiliopoulos & Gareth O. Roberts & Paul Fearnhead, 2006. "Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 333-382, June.
    • Handle: RePEc:bla:jorssb:v:68:y:2006:i:3:p:333-382
    DOI: 10.1111/j.1467-9868.2006.00552.x
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    References listed on IDEAS

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    1. Kalogeropoulos, Konstantinos, 2007. "Likelihood-based inference for a class of multivariate diffusions with unobserved paths," LSE Research Online Documents on Economics 31423, London School of Economics and Political Science, LSE Library.
    2. Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Particle filters for partially observed diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 755-777, September.
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