Algorithms in sequence analysis

Entry requirements: Basic knowledge of probability theory, statistics and programming

Credits: 5

Semester: 3

Course: Elective

Language of the course: English

Objectives

It provides tools for empirical work with time series data and is an introduction into the theoretical foundation of time series models.

Contents

The course provides a survey of the theory and application of time series methods in different areas of life. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks.

Format

Lectures and practice assigments

Assessment

Grading: 40% individual assignments; 40% tests; 20% final work.