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
Прогр. обучения:
Финансовый аналитик
Язык:
английский
Кредиты:
6
Контактные часы:
72
Course Syllabus
Abstract
The course will be a core one for the Banking Institute Master program “Financial Analyst”. The course is intended for studying during the first and the second semester of the Master level education. The course is a prerequisite for some both core and specialized courses of the curriculum. Because of study of material of the course, a student should master and be able to prove the basic facts of strict development of classical econometrics. She/he should also know main ideas of univariate and multivariable time-series analysis including Box-Jenkins approach, ARIMA (p, d, q) models, non-stationary time-series, unit root tests, co-integration, VAR and VECM.
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
- 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.
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
- 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
- Badi H. Baltagi. (2011). Econometrics. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sptbec.978.3.642.20059.5
- 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
- Elhorst, J. P. (DE-588)171025091, (DE-576)131852809. (2014). Spatial econometrics : from cross-sectional data to spatial panels / J. Paul Elhorst. Heidelberg [u.a.]: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.396630170
- Greene, W. H. (2015). Econometric analysis. Slovenia, Europe: Prentice-Hall International. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.1BF5A5CA
- Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277008
- Keele, L., & Wiley InterScience (Online service). (2008). Semiparametric Regression for the Social Sciences. Chichester, England: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=231580