We estimate a Bayesian learning model with heterogeneity aimed at explaining expert forecast disagreement and its evolution over horizons. Disagreement is postulated to have three components due to differences in: i) the initial prior beliefs, ii) the weights attached on priors, and iii)...

Monitoring business cycles faces two potentially conflicting objectives: accuracy and timeliness. To strike a balance between the dual objectives, we develop a Bayesian sequential quickest detection method to identify turning points in real time and propose a sequential stopping time as a...

We develop a Markov Chain Monte Carlo algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparametrization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples involving...

We develop a Markov Chain Monte Carlo algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparametrization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples involving...