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Обычная версия сайта
2024/2025

Эконометрика (продвинутый уровень I)

Статус: Маго-лего
Когда читается: 1, 2 модуль
Охват аудитории: для своего кампуса
Язык: английский
Кредиты: 6
Контактные часы: 56

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 course includes linear regressions, Gauss-Markov theorem, generalised least squares estimation, endogeneity, instrumental variables, maximum likelihood estimation, and a panel data introduction. The course will include the use of STATA and MS Excel. Use of R and other statistical analysis software is optional
Learning Objectives

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

Expected Learning Outcomes

  • Students will gain knowledge on the fundamentals of econometrics and its application
  • Students will gain 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
Course Contents

Course Contents

  • Introduction
  • Matrix algebra
  • Theory of probabilities and statistics. Estimation and inference.
  • The linear regression model. Least squares. Goodness-of-fit and analysis of variance.
  • The Gauss-Markov theorem. Linear hypothesis testing.
  • Interpreting and comparing regression models. Functional form and structural change. Multicollinearity. Nonlinear models
  • Heteroskedasticity. Generalized least squares.
  • Autocorrelation. Testing for first order autocorrelation.
  • Endogeneity, instrumental variables and GMM
  • Panel data models. Introduction
  • Maximum likelihood estimation and specification tests.
Assessment Elements

Assessment Elements

  • non-blocking Домашнее задание
    The first homework (HW1, 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 (HW2, Module 2) 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.
  • non-blocking Тест
    An intermediate test (IT, Module 1) includes tests and problems on the topics 4-6.
  • non-blocking Домашнее задание
    The first homework (HW1, 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 (HW2, Module 2) 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.
  • non-blocking Тест
    Exam (E) includes tests and problems on the topics of the course
Interim Assessment

Interim Assessment

  • 2024/2025 2nd module
    0.1 * Домашнее задание + 0.2 * Домашнее задание + 0.4 * Тест + 0.3 * Тест
Bibliography

Bibliography

Recommended Core Bibliography

  • A guide to modern econometrics, Verbeek, M., 2012

Recommended Additional Bibliography

  • Econometric analysis of panel data, Baltagi, B. H., 2005
  • Econometric analysis, Greene, W. H., 2012

Authors

  • Шевелев Максим Борисович
  • KOTYRLO ELENA STANISLAVOVNA