I use Bayesian VARs with stochastic volatility to forecast global temperatures and sea level and ice cover in the Northerin emisphere until 2010, by exploiting (i) their long-run equilibrium relationship with climate change drivers (CCDs) and (ii) the relationship between world GDP and...

This chapter reviews Bayesian methods for inference and forecasting with VAR models. Bayesian inference and, by extension, forecasting depends on numerical methods for simulating from the posterior distribution of the parameters and special attention is given to the implementation of the...

A semiparametric multiplicative error model (MEM) is proposed. In traditional MEM, the innovations are typically assumed to be Gamma distributed (with one free parameter that ensures unit mean of the innovations and thus identifiability of the model), however empirical investigations unveils the...

In econometrics, Autoregressive Conditional Duration (ACD) models use high-frequency economic or financial duration data, which mostly exhibit irregular time intervals. The ACD model is widely used to examine the duration of transaction volume and duration of price variations in stock markets....