Phenomena such as the Great Moderation have increased the attention of macro-economists towards models where shock processes are not (log-) normal. This paper studies a class of discrete-time rational expectations models where the variance of exogenous innovations is subject to stochastic regime...

We study the relationship between monetary policy and long-term rates in a structural, general equilibrium model estimated on both macro and yields data from the United States. Regime shifts in the conditional variance of productivity shocks, or "uncertainty shocks", are an important model...

We estimate the approximate nonlinear solution of a small DSGE model on euro area data, using the conditional particle filter to compute the model likelihood. Our results are consistent with previous findings, based on simulated data, suggesting that this approach delivers sharper inference...

We develop a time-varying transition probabilities Markov Switching model in which inflation is characterised by two regimes (high and low inflation). Using Bayesian techniques, we apply the model to the euro area, Germany, the US, the UK and Canada for data from the 1960s up to the present. Our...

This paper develops a multi-way analysis of variance for non-Gaussian multivariate distributions and provides a practical simulation algorithm to estimate the corresponding components of variance. It specifically addresses variance in Bayesian predictive distributions, showing that it may be...

In this paper we address the question on whether EMU has amplified or dampened intra euro area divergencies, by looking at a time-varying VAR model of Italy's relative performance compared with the rest of the euro area, spanning from 1976 to 2009. Our main result is that EMU does not appear to...

Prediction of macroeconomic aggregates is one of the primary functions of macroeconometric models, including dynamic factor models, dynamic stochastic general equilibrium models, and vector autoregressions. This study establishes methods that improve the predictions of these models, using a...

Suppose a fund manager uses predictors in changing portfolio allocations over time. How does predictability translate into portfolio decisions? To answer this question we derive a new model within the Bayesian framework, where managers are assumed to modulate the systematic risk in part by...

With the aim of constructing predictive distributions for daily returns, we introduce a new Markov normal mixture model in which the components are themselves normal mixtures. We derive the restrictions on the autocovariances and linear representation of integer powers of the time series in...

Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative...