• A
  • A
  • A
  • АБВ
  • АБВ
  • АБВ
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта

Econometrics

2017/2018
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс обязательный
Когда читается:
2-й курс, 3, 4 модуль

Преподаватели

Course Syllabus

Abstract

The course teaches the regression methods for analyzing observational, experimental and quasi-experimental data in economics and management. The goal is to help students develop a solid theoretical background in introductory level econometrics, the ability to implement the techniques and to critically assess empirical studies in economics, marketing and management science. The emphasis is placed on causal inference and methods of coping with endogenous regressors. Students will learn how to use R for econometric modeling thanks to the fact that 60% of the course is spent on in-class R tutorials involving analysis of real-world datasets, as well as Monte-Carlo simulations.
Learning Objectives

Learning Objectives

  • Choose methods adequately corresponding to the objectives of a research project
  • Collect, store, process and analyze data according to high standards
  • Conduct empirical research in economics and management using modern analytic software tools
  • Develop and apply new research methods
  • Solve economic and managerial problems using best practices of data analysis using modern computational tools
Expected Learning Outcomes

Expected Learning Outcomes

  • Choose methods adequately corresponding to the objectives of a research project
  • Collect, store, process and analyze data according to high standards
  • Conduct empirical research in economics and management using modern analytic software tools
  • Develop and apply new research methods
  • Solve economic and managerial problems using best practices of data analysis using modern computational tools
Course Contents

Course Contents

  • Economic questions and data.
  • Review of probability and statistics
  • Linear regression with one regressor: estimation and fit measures Tutorial: Simple linear regression
  • Linear regression with one regressor: confidence intervals and hypotheses testing
  • Linear regression with multiple regressors: estimation and fit measures
  • Linear regression with multiple regressors: confidence intervals and hypotheses testing
  • Nonlinear regression functions
  • Assessing studies based on multiple regression
  • Regression with panel data
  • Regression with binary dependent variable
  • Instrumental variables
  • Experiment and Quasi-Experiments
  • Time series analysis and forecasting
Assessment Elements

Assessment Elements

  • non-blocking In-class Tests
  • non-blocking DataCamp course (DC)
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.14 * DataCamp course (DC) + 0.3 * Exam + 0.56 * In-class Tests
Bibliography

Bibliography

Recommended Core Bibliography

  • Chapman, C., & Feit, E. M. (2015). R for Marketing Research and Analytics. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=964737

Recommended Additional Bibliography

  • Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics, Update, Global Edition (Vol. Updated third edition). Boston: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419285