2025/2026
Econometrics (Advanced Level)
Type:
Mago-Lego
Delivered by:
HSE Banking Institute
Where:
HSE Banking Institute
When:
2, 3 module
Open to:
students of one campus
Instructors:
Anastasia Byvaltseva-Stankevich
Language:
English
ECTS credits:
6
Contact hours:
72
Course Syllabus
Abstract
This master-level course in Econometrics gives Financial Analyst students an overview of the statistical methods used for empirical analysis in economics and finance. Emphasis of the course is on building a clear understanding of the assumptions behind common estimators, on testing those assumptions, and on selecting and estimating appropriate models for different types of data. The course combines theoretical foundations with practical skills in R statistical software.
Learning Objectives
- As a result of completing the course, students will develop practical skills in implementing econometric methods, interpreting output, and communicating empirical results to practitioners and policymakers. They will be able to design, estimate and critically interpret empirical studies relevant to analysis and decision making in economics and finance
Expected Learning Outcomes
- Course gives opportunities to students to study how to apply econometrics and statistical software to model economic and financial processes, justify causal relations, find their main determinants and make forecasts.
- Students explain the goals of econometric analysis and distinguish econometrics from machine-learning approaches.
- Students derive and interpret OLS estimators in simple and multiple regression settings and assess their properties under different assumptions
- Students formulate the Gauss-Markov theorem, diagnose its violations in the model, select appropriate methods for dealing with violations
- Students choose appropriate estimation methods for regression models with discrete dependent variables of different types
- Students choose among different model specifications for panel data and perform relevant tests to support model choice
- Students use R statistical software to estimate models, produce diagnostics, and generate visualizations of empirical results
- Students design an empirical study using econometric methods
- Students present econometric findings clearly to the audience
- Students critically evaluate empirical papers and reports
Course Contents
- Introduction
- Math terminology
- Data terminology
- Analysis without regression
- Linear regression
- Testing hypothesis based on regression results
- Modifying regression equation
- Variable selection. Multicollinearity
- Non-spherical errors: heteroscedasticity and autocorrelation
- Endogeneity
- Maximum likelihood (ML) estimation
- Binary choice models
- Multiple choice models (introduction, optional)
- Incomplete sample (introduction, optional)
- Count dependent variable models (introduction, optional)
- Panel data models (introduction)
- Impact assessment (introduction, optional)
- Time series analysis (introduction, optional).
Assessment Elements
- Paper review (in-class presentation)
- Midterm (in-class written assignment)
- Project home assignment
- Project peer review (written)
- Exam (in-class written assignment)
Interim Assessment
- 2025/2026 3rd module0.35 * Exam (in-class written assignment) + 0.25 * Midterm (in-class written assignment) + 0.1 * Paper review (in-class presentation) + 0.2 * Project home assignment + 0.1 * Project peer review (written)
Bibliography
Recommended Core Bibliography
- Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics : An Empiricist’s Companion. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=329761
- Greene, W. H. (2012). Econometric Analysis: International Edition : Global Edition (Vol. 7th ed., International ed). Boston: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1417839
- Introductory econometrics : a modern approach, Wooldridge, J. M., 2009
- Jeffrey M. Wooldridge. (2019). Introductory Econometrics: A Modern Approach, Edition 7. Cengage Learning.
- Verbeek, M. (2004). A Guide to Modern Econometrics (Vol. 2nd ed). Southern Gate, Chichester, West Sussex, England: John Wiley and Sons, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=108185
Recommended Additional Bibliography
- Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics : Methods and Applications. New York, NY: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=138992