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ADVANCED TOPICS IN TIME SERIES

1st section

Syllabus

EN IT

Learning Objectives

LEARNING OUTCOMES: The objective of this module is to introduce
students to advanced topics in time series analysis to enhance independent research. Examples of active topic of research will be provided during the lectures.

KNOWLEDGE AND UNDERSTANDING:
Thee course provides the students with the theoretical and practical undergrounds of advance time series analyses, for Macro and Financial time series.

APPLYING KNOWLEDGE AND UNDERSTANDING:The students will be able to produce
and interpret advance statistical analyses.

MAKING JUDGEMENTS: Students will gain the ability to make judgments about implications of the statistical results for the issue at hand.

COMMUNICATION SKILLS: Students will be able to present and communicate effectively the results of the empirical analyses on time series data.

LEARNING SKILLS: Students will have the ability to develop and increase their skills through the consultation of the published scientific literature and the use of databases and other information.


GIANLUCA CUBADDA

Prerequisites

LM in Economics students: Time Series
LM in Finance & Banking students: Time Series and Econometrics

Program

1) Single equation dynamic models; Dynamic factor models (first week);
2) Regularization for large dynamic regression models; dimension reduction methods (second week).
3) Applications in macroeconomics and finance; Economic and financial forecasting (third week).

Books

Peña, D, and Tsay, R.S. (2021), Statistical learning for big dependent data, Wiley. New York.

Bibliography

The slides of the course, as well as some papers and computational routines, will be available in the course Teams page.

Teaching methods

Each topic of the course will be covered in class both in a theoretical and in an empirical perspective. Students will be stimulated to an active and lively participation at the lessons.

Exam Rules

The final evaluation is a written examination. Questions will potentially cover all the topics of the course and will be of both theoretical and applied nature. The main goal of the exam is to evaluate both the theoretical knowledge of the methods taught in the course and the student's ability to use them for the empirical analysis of economic and financial phenomena.
The exam cannot be taken twice in the winter session.

STEFANO GRASSI

Program

1. Introduction
• General review of time series models.
2. Filtering theory Part 1
• Linear filters
3. Filtering theory Part 2
• State-space form
• Kalman filter and Smoother
• Some economic examples
4. Estimation
• Maximum likelihood
• Bayesian estimation
• DSGE. Time Varying Parameters VAR.

Books

• Durbin, J., and Koopman, S.J. (2001), Time Series Analysis by State Space Methods, Oxford University Press, Oxford, UK.
• Harvey, A.C. (1989), Forecasting, Structural Time Series and the Kalman Filter, Cambridge University Press, Cambridge, UK.