development of adjacent age groups is assured allowing for consistent forecasts. We develop an appropriate Markov Chain Monte … Carlo algorithm to estimate the parameters and the latent variables in an efficient one-step procedure. Via the Bayesian …

We develop Bayesian techniques for estimation and model comparison in a novel Generalised Stochastic Unit Root (GSTUR …

The Reversible Jump Markov Chain Monte Carlo (RJMCMC) method can enhance Bayesian DSGE estimation by sampling from a … method to jointly sample from an ARMA process of unknown order along with the associated parameters. We apply the method to …

around the deterministic steady state, and a function iterator using a multivariate global Chebyshev polynomial approximation …

We use a Bayesian dynamic factor model to measure Germany’s pre World War I economic activity. The procedure makes …

We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse …

We analyze the impact of short-run economic fluctuations on age-specific mortality using Bayesian time series …

the underlying yield factors. We propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently estimate the SVNS …In this paper, we develop and apply Bayesian inference for an extended Nelson- Siegel (1987) term structure model … work efficiently and is easily adapted to alternative specifications of dynamic factor models revealing (multivariate …

development of adjacent age groups is assured allowing for consistent forecasts. We develop an appropriate Markov Chain Monte … Carlo algorithm to estimate the parameters and the latent variables in an efficient one-step procedure. Via the Bayesian …

available. For this situation, we employ a Bayesian quadrature method because it allows us to incorporate prior assumptions on …