Multivariate Econometrics

2018-2019

Course Objective

This course introduces students to advanced econometric theory and
methods for analyzing linear multivariate non-stationary time-series and
panel data.

By the end of this course, participants will:
(1) have detailed knowledge of VAR, VECM and dynamic panel-data models.
(2) understand the limit theory behind spurious regression and
cointegration;
(3) be familiar with advanced unit-root and cointegration tests;
(4) understand the challenges in designing, estimating and analyzing
linear econometric models for non-stationary time-series and panel data.

Course Content

This course covers both theoretical and practical aspects of modeling
multivariate non-stationary time-series and panel data, with special
emphasis on unit-root processes and cointegration.

The students will be introduced to linear multivariate time-series
models and linear panel data models used in econometrics. Important
topics include marginalizing, conditioning, exogeneity, vector
autoregressive (VAR) models, and vector error correction models (VECM).

Important limit results will be carefully derived providing the students
with a deep understanding of the theory and practice behind a wide range
of advanced unit roots test, spurious regression, cointegration, and
dynamic panels.

Teaching Methods

Lectures and tutorials

Method of Assessment

Final exam and group assignment – Individual assessment

Literature

Main textbook:
Davidson (2000), Econometric Theory, Blackwell publishing;

Supplementary textbooks:
Lutkepohl (2005), New Introduction to Multiple Time Series Analysis,
Springer;
Hamilton (1994), Time Series Analysis, Princeton University Press;
Pesaran (2015), Time series and Panel Data Econometrics, Oxford
University Press;
Baltagi (2005), Econometric Analysis of Panel Data, Third edition, John
iley & Sons, Ltd;

Other reading materials:
Lecture notes provided by the teacher;
Journal articles recommended by the teacher: will be uploaded under the
module: Articles to read

Recommended background knowledge

This course presumes that the students are familiar with econometric
methods, probability theory, mathematical statistics. The theory and
practice behind the regression model should be well understood.
Furthermore, the students should have been introduced to time-series
analysis. In particular, the students should be familiar with the
concepts of stationarity and ARMA models.

General Information

Course Code E_EORM_MVE
Credits 6 EC
Period P2
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. H. Karabiyik
Examiner dr. H. Karabiyik
Teaching Staff dr. H. Karabiyik

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: