Bayesian analysis of the stochastic conditional duration model

DOI10.1016/j.csda.2005.07.005zbMath1445.62241OpenAlexW2139918108MaRDI QIDQ959312

Chris M. Strickland, Gael M. Martin, Catherine S. Forbes

Publication date: 11 December 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2005.07.005

zbMATH Keywords

Markov chain Monte Carlolatent variable modelKalman filter and simulation smoothernon-Gaussian state space modeltransaction data

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15)

Related Items (16)

Modeling dynamic effects of promotion on interpurchase times ⋮ Efficient data augmentation techniques for some classes of state space models ⋮ Time-varying autoregressive conditional duration model ⋮ Automated variable selection in vector multiplicative error models ⋮ Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach ⋮ Estimation of the stochastic conditional duration model via alternative methods ⋮ Feasible parameter regions for alternative discrete state space models ⋮ The HESSIAN method: highly efficient simulation smoothing, in a nutshell ⋮ Efficient importance sampling for ML estimation of SCD models ⋮ Efficient Bayesian estimation of multivariate state space models ⋮ A QUASI-LOCALLY MOST POWERFUL TEST FOR CORRELATION IN THE CONDITIONAL VARIANCE OF POSITIVE DATA ⋮ Bayesian estimation and inference for log-ACD models ⋮ MCMC methods to approximate conditional predictive distributions ⋮ Bayesian inference of asymmetric stochastic conditional duration models ⋮ Editorial: 2nd special issue on statistical signal extraction and filtering ⋮ Parameterisation and efficient MCMC estimation of non-Gaussian state space models

Uses Software

• BUGS

Cites Work

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