Title: Multivariate Time Series: Bayesian Dynamic Modelling & Forecasting Prof. Mike West, Duke University, USA. Email: Contact

Summary: I will review core perspectives of Bayesian modelling, analysis and forecasting of multivariate time series using state-space models. Following a basic review of dynamic linear models, sequential filtering, forecasting and batch analysis in a Bayesian framework, I will touch on several widely-used classes of structured, multivariate dynamic models: this will include time-varying vector autoregressions, matrix-variate models, dynamic factor models, and multivariate volatility models. I will then contact some areas of development over the last decade that respond to the need to define increasingly parsimonious models as time series dimension increases; this will include sparsity modelling using graphical models and other approaches. I will discuss examples from a range of application areas including econometrics, finance, and biomedical studies, and conclude by contacting more recent modelling developments and current research frontiers.

Title: Component-based path modeling.
Prof. Vincenzo Esposito Vinzi, Essec Business School, France. Email: Contact

Summary: This tutorial covers the main statistical approaches to modeling networks of dependence (predictive) relationships between multiple blocks of manifest variables through linear composites. These approaches include Partial Least Squares Path Modeling but also its most recent variants and alternatives such as Generalized Structured Component Analysis and Regularized Generalized Canonical Correlation Analysis among others. The presented methods aim at optimizing different criteria related to correlations/covariances between composites, explained variances of endogenous blocks, or redundancies between blocks in coherence with the prediction flow specified by a path diagram.