The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

A structural break is viewed as a permanent change in the parameter vector of a model.
Using taxonomies of all sources of forecast errors for both conditional mean and conditional …

This article explains the foundational concepts of Bayesian data analysis using virtually no
mathematical notation. Bayesian ideas already match your intuitions from everyday …

Provides statistical tools and techniques needed to understand today's financial markets The
Second Edition of this critically acclaimed text provides a comprehensive and systematic …

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a
unified, practical likelihood-based framework for the analysis of stochastic volatility models …

This article analyses the recently suggested particle approach to filtering time series. We
suggest that the algorithm is not robust to outliers for two reasons: The design of the …

This text is concerned with Bayesian learning, inference and forecasting in dynamic
environments. We describe the structure and theory of classes of dynamic models and their …

This book contributes to re cent developments on the statistical analysis of multiple time
series in the presence of regime shifts. Markov-switching models have become popular for …

This book provides a formal analysis of the models, procedures, and measures of economic
forecasting with a view to improving forecasting practice. David Hendry and Michael …

We examine autoregressive time series models that are subject to regime switching. These
shifts are determined by the outcome of an unobserved two-state indicator variable that …