Магистратура 2024/2025

Эконометрика (продвинутый уровень II)

Статус: Курс обязательный (Экономика и экономическая политика)

Направление: 38.04.01. Экономика

Кто читает: Департамент прикладной экономики

Где читается: Факультет экономических наук

Когда читается: 1-й курс, 3, 4 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Котырло Елена Станиславовна, Семерикова Елена Вячеславовна, Скроботов Антон Андреевич, Слаболицкий Илья Сергеевич

Прогр. обучения: Экономика и экономическая политика

Язык: английский

Кредиты: 6

Course Syllabus

Abstract

The course “Advanced Econometrics II” 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 is focused on issues in limited variables models, time series models; dynamic panel data models; policy evaluation; generalised method of moments; nonparametric and semiparametric models. Optional topics are duration models, spatial econometrics, quantile regression, Bayesian estimation, big data analysis. 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 “Advanced Econometrics II” focuses on the estimation, inference and identification of regression models.

Expected Learning Outcomes

• Students will be capable of learning and developing skills in using the statistical software STATA for econometric analysis
• Students will acquire an understanding of the basics of econometrics and its application
• Students will be able to practice performing data analysis and applying econometric tools in research and analytics

Course Contents

• Binary choice models.
• Multi-response models. Models for count data
• Tobit models. Sample selection bias
• Estimating treatment effects.
• Univariate time series models
• Choosing ARMA model and its estimation
• Autoregressive conditional heteroskedasticity (ARCH).
• Multivariate time series models
• Dynamic linear models
• Non-parametric and semiparametric methods
• Duration models
• Simulation-based estimation. Bootstrap standard errors
• Bayesian estimation and inference
• Spatial econometrics
• Nonlinear regression models. Quantile regression
• Shrinkage methods

Assessment Elements

• Intermediate Test
  Intermediate Test (IT, Module 3) includes test and problems on the topics 1-4.
• Homework 1
  Homework 1 (HW1, 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 1-4).
• Homework 2
  Homework (HW2, Module 4) includes empirical justification of hypotheses relevant to time series analysis and panel data analysis. It is based on the material of the topics 5-9.
• Activity on classes
  Maximum of activity score is 10 and ranged on the base of the average activity in the class. Student is marked to be active on a class if he\she solves problems at home and at the (black-) whiteboard.
• Exam
  Exam includes tests and problems on the topics 5-16

Interim Assessment

• 2024/2025 4th module
  0.06 * Activity on classes + 0.06 * Activity on classes + 0.4 * Exam + 0.09 * Homework 1 + 0.09 * Homework 2 + 0.3 * Intermediate Test

Bibliography

Recommended Core Bibliography

• A guide to modern econometrics, Verbeek, M., 2013
• 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

• 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
• Econometric analysis of panel data, Baltagi, B. H., 2013
• 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
• 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
• Microeconometrics : methods and applications, Cameron, A. C., 2009
• Quantile regression, Koenker, R., 2005
• Semiparametric regression for the social sciences, Keele, L., 2008

Authors

• KOTYRLO ELENA STANISLAVOVNA