This paper develops an efficient approach to model and forecast time-series data with an unknown number of change-points. Using a conjugate prior and conditional on time-invariant parameters, the predictive density and the posterior distribution of the change-points have closed forms. The...

In this paper we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional,...

This paper proposes new dynamic component models of realized covariance (RCOV) matrices based on recent work in time-varying Wishart distributions. The specifications are linked to returns for a joint multivariate model of returns and covariance dynamics that is both easy to estimate and...

We propose a new discrete-time model of returns in which jumps capture persistence in the conditional variance and higher-order moments. Jump arrival is governed by a heterogeneous Poisson process. The intensity is directed by a latent stochastic autoregressive process, while the jump-size...

This paper develops two methods for estimating the effect of schooling on achievement test scores that control for the endogeneity of schooling by postulating that both schooling and test scores are generated by a common unobserved latent ability. These methods are applied to data on schooling...

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and...

This paper proposes a new dynamic model of realized covariance (RCOV) matrices based on recent work in time-varying Wishart distributions. The specifications can be linked to returns for a joint multivariate model of returns and covariance dynamics that is both easy to estimate and forecast....

This note is made of four book reviews of Brooks et al. (2011), Karian and Dudewicz (2011), McGrayne (2010), and Ziliak and Mc- Closkey (2008), respectively. They are scheduled to appear in the next issue of CHANCE.

Abstract: In this paper, we review classical and advanced methodologies for analysing within-subject functional Magnetic Resonance Imaging (fMRI) data. Such data are usually acquired during sensory or cognitive experiments that aims at stimulating the subject in the scanner and eliciting evoked...