Магистратура
2024/2025
Эконометрика (продвинутый уровень)
Статус:
Курс обязательный (Финансовый аналитик)
Направление:
38.04.08. Финансы и кредит
Кто читает:
Банковский институт
Где читается:
Банковский институт
Когда читается:
1-й курс, 2, 3 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Семерикова Елена Вячеславовна
Прогр. обучения:
Финансовый аналитик
Язык:
английский
Кредиты:
6
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
- Matrix algebra
- Probability theory of and statistics.
- The linear regression model. Least squares. Goodness-of-fit and analysis of variance
- The Gauss–Markov assumptions. Linear hypothesis testing
- Interpreting and comparing regression models
- Heteroskedasticity. Generalized least squares
- Autocorrelation.
- Endogeneity, instrumental variables and GMM
- Models based on panel data
- Maximum likelihood estimation and specification tests
- Binary choice models
- Tobit models
- Univariate time series models.
- Choosing ARMA model and its estimation
- Multivariate time series models
- Dynamic linear models.
Assessment Elements
- Intermediate test (mid-term)The intermediate test (IT, Module 2) includes tests and problems on the topics 1-9
- Final examFinal exam includes tests and problems on the topics 10-20. Activity on classes is appreciated: Student is marked to be active on a class if he\she solves problems at home and at the (black-) whiteboard. Activity points might be considered as a bonus for the final grade of the course. A remarkable difference between the activity and the final exam scores may be a reason for additional oral examination at the discretion of the tutor
- Home Assignment
Interim Assessment
- 2024/2025 3rd module0.5 * Final exam + 0.2 * Home Assignment + 0.3 * Intermediate test (mid-term)
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