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It is proposed in the literature that in some complicated problems maximumlikelihood estimates (MLE) are not suitable or even do not exist. Analternative to MLE for estimation of the parameters is the Bayesian method. TheMarkov chain Monte Carlo (MCMC) simulation procedure is designed to fitBayesian models. Bayesian method like classical method (MLE) has advantages anddisadvantages. One of the advantages of Bayesian method over MLE method is theability of saving the information included in past data through the posteriordistributions of the model parameters to be used for modelling future data. Inthis article we investigate the performance of Bayesian method in modellingdynamic binary data when the data are growing over time and individuals.

Bayesian analysis of dynamic binary data: A simulation study and application to economic index SP