We compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain...

Reversible jump methods are the most commonly used Markov chain Monte Carlo tool for exploring variable dimension statistical models. Recently, however, an alternative approach based on birth-and-death processes has been proposed by Stephens for mixtures of distributions. We show that the...

Reversible jump methods are the most commonly used Markov chain Monte Carlo tool for exploring variable dimension statistical models. Recently, however, an alternative approach based on birth-and-death processes has been proposed by Stephens for mixtures of distributions. We show that the...

We compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain...

We develop a new Markov Chain Monte Carlo procedure for a time series regression model truncated by upper and lower bounds. The regression error term is assumed to follow an ARMA--GARCH process. We use a convergence diagnostics with a simultaneous test of mean and covariance stationarity and...

We develop a new Markov Chain Monte Carlo procedure for a time series regression model truncated by upper and lower bounds. The regression error term is assumed to follow an ARMA--GARCH process. We use a convergence diagnostics with a simultaneous test of mean and covariance stationarity and...