Econometrics (Advanced Level I)

2024/2025

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered by:

Department of Applied Economics

Type:

Elective course

When:

1 year, 1, 2 module

Instructors

Kotyrlo, Elena

Semerikova, Elena V.

Slabolitskiy, Ilya S.

Course Syllabus

Abstract

The course “Advanced Econometrics ” focuses on the estimation, inference and identification of regression models. Particular attention is paid to the econometric theory, to the application of econometrics to real-world problems, and to the interpretation of the estimation results. The course includes linear regressions, Gauss-Markov theorem, generalised least squares estimation, endogeneity, instrumental variables, maximum likelihood estimation, and a panel data introduction. The course will include the use of STATA and MS Excel. Use of R and other statistical analysis software is optional

Learning Objectives

• The course aims to provide students with: • knowledge on the fundamentals of econometrics and its application • knowledge and proficiency on the use of statistical package STATA for econometric analysis • practice in conducting data analysis and application of econometric tools in research and analytics

Expected Learning Outcomes

• Students will gain knowledge on the fundamentals of econometrics and its application
• Students will gain knowledge and proficiency on the use of statistical package STATA for econometric analysis
• Practice in conducting data analysis and application of econometric tools in research and analytics

Course Contents

• Introduction
• Matrix algebra
• Theory of probabilities and statistics. Estimation and inference.
• The linear regression model. Least squares. Goodness-of-fit and analysis of variance.
• The Gauss-Markov theorem. Linear hypothesis testing.
• Interpreting and comparing regression models. Functional form and structural change. Multicollinearity. Nonlinear models
• Heteroskedasticity. Generalized least squares.
• Autocorrelation. Testing for first order autocorrelation.
• Endogeneity, instrumental variables and GMM
• Panel data models. Introduction
• Maximum likelihood estimation and specification tests.

Assessment Elements

• Домашнее задание
  The first homework (HW1, Module 1): the course participants propose a hypothesis and collect their own cross-sectional data for a regression model that is going to be analysed further in Module 2. • The second homework (HW2, Module 2) is based on data collected in Module 1 (and approved by a tutor!). It imposes empirical justification of the stated hypotheses on the base of the material of the topics 4-9. Students are expected to use statistical software STATA or another for data analysis.
• Тест
  An intermediate test (IT, Module 1) includes tests and problems on the topics 4-6.
• Домашнее задание
  The first homework (HW1, Module 1): the course participants propose a hypothesis and collect their own cross-sectional data for a regression model that is going to be analysed further in Module 2. • The second homework (HW2, Module 2) is based on data collected in Module 1 (and approved by a tutor!). It imposes empirical justification of the stated hypotheses on the base of the material of the topics 4-9. Students are expected to use statistical software STATA or another for data analysis.
• Тест
  Exam (E) includes tests and problems on the topics of the course

Interim Assessment

• 2024/2025 2nd module
  0.1 * Домашнее задание + 0.2 * Домашнее задание + 0.3 * Тест + 0.4 * Тест

Bibliography

Recommended Core Bibliography

• A guide to modern econometrics, Verbeek, M., 2012

Recommended Additional Bibliography

• Econometric analysis of panel data, Baltagi, B. H., 2005
• Econometric analysis, Greene, W. H., 2012

Authors

• Шевелев Максим Борисович
• KOTYRLO ELENA STANISLAVOVNA