Master 2021/2022

Econometrics (Advanced Level)

Type: Elective course (Economics and Economic Policy)

Area of studies: Economics

Delivered by: Department of Applied Economics

Where: Faculty of Economic Sciences

When: 1 year, 1-4 module

Mode of studies: offline

Open to: students of one campus

Instructors: Elena Kotyrlo, Margarita Maximova, Elena V. Semerikova

Master’s programme: Economics and Economic policy

Language: English

ECTS credits: 12

Contact hours: 140

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 first part of the course (Fall term) includes linear regressions and models with limited dependent data. Topics on Gauss-Markov theorem, endogeneity, instrumental variables, maximum likelihood estimation will be covered. The second part of the course (Spring term) is focused on issues in system of equations; time series models; panel data models; nonparametric and semiparametric models; Bayesian estimation. 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 Prerequisites Course requires knowledge of linear algebra, calculus, probability theory and mathematical statistics.

Expected Learning Outcomes

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

Course Contents

• Part I: Fall term. Topic 1. Introduction
• Part I: Topic 2. Vectors and matrices
• Part I: Topic 3. Statistical and distribution theory
• Part I: Topic 4. An introduction to linear regression
• Part I: Topic 5. The Gauss–Markov assumptions
• Part I: Topic 6. Interpreting and comparing regression models
• Part I: Topic 7. Heteroskedasticity
• Part I: Topic 8. Autocorrelation
• Part I: Topic 9. Endogeneity, instrumental variables and GMM
• Part I: Topic 10. Models based on panel data
• Part I: Topic 11. Maximum likelihood estimation and specification tests.
• Part II. Topic 12. Binary choice models
• Part II. Topic 13. Multi-response models. Models for count data
• Part II. Topic 14. Tobit models
• Part II. Topic 15. Estimating treatment effects
• Part II. Topic 16. Univariate time series models
• Part II. Topic 17. Choosing ARMA model and its estimation
• Part II. Topic 18. Autoregressive conditional heteroskedasticity (ARCH)
• Part II. Topic 19. Multivariate time series models
• Part II. Topic 20. Dynamic linear models
• Part II. Topic 21. Non-parametric and semiparametric methods.
• Part II. Topic 22. Duration models
• Part II. Topic 23. Simulation-based estimation. Bootstrap standard errors
• Part II. Topic 24. Bayesian estimation and inference
• Part II. Topic 25. Spatial econometrics
• Part II. Topic 26. Nonlinear regression models. Quantile regression
• Part II. Topic 27. Shrinkage methods.

Assessment Elements

• The cumulative score for the Fall term
• The final grade for the Spring term (FG)
  Экзамен проводится в письменной форме. Экзамен проводится на платформе Webinar.ru. К экзамену необходимо подключиться за 5 минут до начала. Компьютер студента должен удовлетворять требованиям: подключение через Google Chrome. Для участия в экзамене студент обязан отправить актуальный e-mail адрес преподавателю за неделю до экзамена. Во время экзамена студентам разрешено пользоваться калькулятором, статистическими программами. Файл с экзаменационными задачами и вставленными в выделенные ячейки ответами и скриншоты с решением должны быть отправлены преподавателю по e-mail не позднее установленного времени. Если возникает проблема отправки или письмо с вложением не было доставлено вовремя, студенту надо будет подтвердить факт своевременной отправки, прислав соответствующие скриншоты. Процедура пересдачи аналогична процедуре сдачи.
• The first intermediate test (Module 1)
  includes tests and problems on the topics 4-6
• The first homework (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 ( Module 2)
  It 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.
• Midterm exam
  it includes tests and problems on the topics 4-11
• Activity on classes
• The second Intermediate Test (Module 3)
  it includes test and problems on the topics 11-15
• The third homework (Module 3)
  The course participants may collect their own data or relay on data given by tutors and use the statistical package STATA (another software is optional) for data analysis. Econometric techniques are based on the topics 10-15).
• The forth homework (Module 4)
  it includes empirical justification of hypotheses relevant to time series analysis and panel data analysis. It is based on the material of the topics 16-19.
• Final exam
  it includes tests and problems on the topics 16-27.

Interim Assessment

• 2021/2022 2nd module
  0.1 * The first intermediate test (Module 1) + 0.25 * The cumulative score for the Fall term + 0.5 * The first homework (Module 1) + 0.1 * The second homework ( Module 2) + 0.05 * The final grade for the Spring term (FG)
• 2021/2022 4th module
  0.06 * The first homework (Module 1) + 0.42 * The final grade for the Spring term (FG) + 0.06 * The first intermediate test (Module 1) + 0.06 * The cumulative score for the Fall term + 0.4 * The second homework ( Module 2)

Bibliography

Recommended Core Bibliography

• Verbeek, M. (2017). A Guide to Modern Econometrics (Vol. 5th edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639496

Recommended Additional Bibliography

• 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
• Härdle, W., Müller, M., Sperlich, S. A., & Werwatz, A. (2004). Nonparametric and Semiparametric Models. Switzerland, Europe: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.121C8F13
• 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
• Koenker, R. (2005). Quantile Regression. Cambridge: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=139750

Presentation

Authors

• KOTYRLO ELENA STANISLAVOVNA
• SIDOROVA ELENA EVGENEVNA