Time series - 2022

Prof.ssa Alessandra Luati

Module 1: state space models and recent developments

• Linear Gaussian models and the Kalman filter 
• Maximum likelihood estimation
• Non linear non Gaussian models
• Score driven models
• Maximum likelihood estimation
• Illustrations

Prof. Greta Goracci

Module 2: Introduction to nonlinear time series

• Limitation of linear models
• Some parametric nonlinear time series models
• Stationary distributions
• Inference

Prof. Simone Giannerini

Module 3: Nonlinear time series and chaos theory 

• Dynamical systems
• Initial value sensitivity in dynamical systems
• The embedding theorem
• Initial value sensitivity in random dynamical systems 
• Statistical problems: estimation and testing
• Some examples