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

transition autoregressive models or time varying parameter models). Bayesian econometric methods for inference are developed for …

transition autoregressive models or time varying parameter models). Bayesian econometric methods for inference are developed for …

Carlo algorithm to estimate the parameters and the latent variables in an efficient one-step procedure. Via the Bayesian …

Bayesian statistical methods are naturally oriented towards pooling in a rigorous way information from separate sources … volatility or options prices. We develop a formal Bayesian framework where we can merge the backward looking information as … forecasting options prices out of sample (i.e. one-day ahead) our Bayesian estimators outperform standard forecasts that use …

Employing a Bayesian approach, we investigate the impact of international business cycles on the UK economy in the …

This paper makes use of the novel Generalized Stochastic Unit Root (GSTUR) model, Bayesian model estimation and model …

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross …

Floor and Ceiling model. Bayesian and classical methods for estimation and testing are developed and compared in the context … of an application involving U.S. macroeconomic data. In terms of statistical significance both classical and Bayesian …