Master
2024/2025
Econometrics (Advanced Level I)
Type:
Elective course (Strategic Corporate Finance)
Area of studies:
Finance and Credit
Delivered by:
Department of Applied Economics
Where:
Faculty of Economic Sciences
When:
1 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Master’s programme:
Strategic Corporate Finance
Language:
English
ECTS credits:
6
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 заданиеThe 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.
- ТестAn intermediate test (IT, Module 1) includes tests and problems on the topics 4-6.
- Домашнее заданиеThe 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.
- ТестExam (E) includes tests and problems on the topics of the course
Interim Assessment
- 2024/2025 2nd module0.2 * Домашнее задание + 0.1 * Домашнее задание + 0.4 * Тест + 0.3 * Тест
