Bayesian Analysis of the Stochastic Conditional Duration Model Strickland, Chris M. Forbes, Catherine S. Martin, Gael M. 10.4225/03/5934f385d5bce https://bridges.monash.edu/articles/journal_contribution/Bayesian_Analysis_of_the_Stochastic_Conditional_Duration_Model/5073496 A Bayesian Markov Chain Monte Carlo methodology is developed for estimating the stochastic conditional duration model. The conditional mean of durations between trades is modelled as a latent stochastic process, with the conditional distribution of durations having positive support. The sampling scheme employed is a hybrid of the Gibbs and Metropolis Hastings algorithms, with the latent vector sampled in blocks. The suggested approach is shown to be preferable to the quasi-maximum likelihood approach, and its mixing speed faster than that of an alternative single-move algorithm. The methodology is illustrated with an application to Australian intraday stock market data. 2017-06-05 06:00:36 Transaction data Kalman filter and simulation smoother monash:2332 1959.1/2332 Latent factor model 2003 Non-Gaussian state space model