A multimove sampling scheme for the state parameters of non-Gaussian and nonlinear dynamic models for univariate time series is proposed. This procedure follows the Bayesian framework, within a Gibbs sampling algorithm with steps of the Metropolis–Hastings algorithm. This sampling scheme...

This paper builds on Asai and McAleer (2009) and develops a new multivariate Dynamic Conditional Correlation (DCC) model where the parameters of the correlation dynamics and those of the log-volatility process are driven by two latent Markov chains. We outline a suitable Bayesian inference...

Personality, ability, trust, motivation and beliefs determine outcomes in life and in particular those of economic nature such as finding a job or earnings. A problem with this type of determinants is that they are not immanently objectively quantifiable and that there is no intrinsic scale -...

In this work we propose a model for the intensity of a space–time point process, specified by a sequence of spatial surfaces that evolve dynamically in time. This specification allows flexible structures for the components of the model, in order to handle temporal and spatial variations both...

We propose and estimate a new class of equity return models that incorporate scale mixtures of the skew-normal distribution for the error distribution into the standard stochastic volatility framework. The main advantage of our models is that they can simultaneously accommodate the skewness,...

The approximation of G/G/s models from Markov models M/M/s has been studied in the literature. The study of a queue model is detailed in the present article, using times of arrivals and time service distributed by Weibull whose estimation of parameters was performed with the Bayesian method...