Introduction to Time Series and Dynamic Econometrics

2019-2020

Course Objective

This course introduces students to time series analysis and dynamic
econometric models for economics, business and finance.

Course Content

This course covers both theoretical and practical aspects of time series
econometrics including the analysis of stationary and non-stationary
stochastic processes in economics, business and finance.

The students are introduced to autoregressive moving average (ARMA)
models, autoregressive distributed lag (ADL) models, and error
correction models (ECM). Furthermore, the course provides both
theoretical and practical insight into parameter estimation in
time-series and the use of these models for forecasting, testing for
Granger causality, and performing policy analysis using impulse response
functions.

Finally, students become familiar with the fundamental problem of
spurious regression in time-series analysis. We find a solution to this
problem by taking a journey into the theory and practice behind
unit-root tests, cointegration tests and error-correction representation
theorems.

Teaching Methods

Lectures and practical classes. During practical classes time will be
made for discussing exercises.

Method of Assessment

Final exam and group assignments – Individual assessment.

Literature

All relevant material can be found in the lecture notes and other study
material provided by the teacher.

Recommended optional reading material:

J. Stock and M. Watson, 2011, Introduction to Econometrics. Prentice
Hall.

P. Brockwell and R. Davis, 2010, Introduction to Time Series and
Forecasting. Springer.

C. Brooks, 2014, Introductory Econometrics for Finance. Cambridge
University Press.

Target Audience

This course in the minor Applied Econometrics is targeted at both
econometrics and non-econometrics students that have knowledge of basic
mathematics, probability and statistics.

Recommended background knowledge

This course builds on the foundations laid either in the sequence of
courses in `Kwantitatieve Methoden` (in the Economics programme) or in
that of `Statistics` and `Business Mathematics` (in the Business
Administration programme). It assumes familiarity with probability
and statistics. This material corresponds more or less to Part I
(Chapters1-3) in Stock & Watson, and students are recommended to refresh
their memory on this prior to the first lecture.

General Information

Course Code E_EOR3_ITSDE
Credits 6 EC
Period P1
Course Level 300
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator prof. dr. S.J. Koopman
Examiner prof. dr. S.J. Koopman
Teaching Staff prof. dr. S.J. Koopman

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Study Group, Lecture
Target audiences

This course is also available as: