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Discussion on the paper of 'Particle Markov chain Monte Carlo methods' by Christophe Andrieu, Arnaud Doucet, and Roman Holenstein

Research output: Contribution to journalComment/debatepeer-review

<mark>Journal publication date</mark>2010
<mark>Journal</mark>Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Issue number3
Number of pages3
Pages (from-to)308-310
Publication StatusPublished
<mark>Original language</mark>English


We congratulate the authors for a remarkable paper, which addresses a problem of fundamental practical importance: parameter estimation in state space models by using sequential Monte Carlo (SMC) algorithms. In Belmonte et al. (2008) we fit duration state space models to high frequency transaction data and we require a computational methodology that can handle efficiently time series of length T =O.104–105/. We have experimented with particle Markov chain Monte Carlo (PMCMC) methods and with the smooth particle filter (SPF) of Pitt (2002). The latter is also based on the use of SMC algorithms to derive maximum likelihood parameter estimates; it is, however, limited to scalar signals. Therefore, in the context of duration modelling this limitation rules out multifactor or multi-dimensional models, and we believe that PMCMC methods can be very useful in such cases.

Bibliographic note

The paper was read before The Royal Statistical Society at a meeting organized by the Research Section on Wednesday, October 14th, 2009.