90481 - Advanced Time Series

Learning outcomes

By the end of the course the student is able to analyse data generated by GARCH, DCS, long memory processes and make inference on the moment estimators.

Course contents

Prerequisites: linear time series analysis (linear processes, Wold representation, autocovariance and spectral density function, inference on the moments of a linear process) and basic stochastic processes (conditioning, martingales, martingale difference sequences).

Topics covered throughout the course: Time varying parameter models. Parameter driven models: state space models and the Kalman filter. Observation driven models:  GARCH processes and Score driven models. Estimation and inference.

Optional, additional topics: dynamic quantiles, forecast densities, invertibility, long memory processes, locally stationary processes.

Readings/Bibliography

Brockwell P.J. and Davis R.A. (1991), Time series: Theory and Methods, Springer.

Further readings will be suggested during the course.

Teaching methods

Recorded lectures, lectures and discussions in class exercises and lab sessions (R, Matlab or Python).

Assessment methods

1) written exam

2) analysis of a case study (coursework, take home) 

 

Teaching tools

Textbook, notes and papers that can be found on the institutional teacher web-site and in Virtuale

Office hours

See the website of Alessandra Luati