market. The identification of structural shocks is based on sign restrictions. We identify four structural shocks: a labour …

accounted for parameter uncertainty using Bayesian Model Averaging (BMA). Though BMA-based models of exchange rates tend to …

assumption, since point estimators are biased and confidence intervals are difficult to construct. From the Bayesian perspective … treatment effect can be obtained with Bayesian simulation techniques. Our Bayesian inference is applied to an empirical problem … monotone treatment response assumption yield good identification power. …

uncertainty and deviation from normality in a Bayesian framework. The non-normal asset returns are modeled as finite Gaussian … portfolio weights are then constructed so as to maximize agents’ expected utility. Simple experiment suggests that our Bayesian …

There has been a call for caution when using the conventional method for Bayesian inference in setidentified structural …

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 …

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