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Regular version of the site
Master 2022/2023

Econometrics I (advanced level)

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Area of studies: Economics
When: 1 year, 2 module
Mode of studies: offline
Open to: students of one campus
Master’s programme: Applied Economics and Mathematical Methods
Language: English
ECTS credits: 3
Contact hours: 24

Course Syllabus

Abstract

The main goal is to familiarize the students with advanced methods of econometric research in economics and finance. In particular, the course accentuates the problem of endogeneity and the ways to address it in the analysis of cross-sectional and panel data. The course is of applied nature: The lectures are supplemented by exercises in the computer lab. The topics covered include: A review of the classical linear regression model; Causes and consequences of endogeneity; Instrumental variables methods; Key panel data techniques; Difference-in-difference estimation techniques; An overview of the matching models and regression discontinuity designs. Computer exercises using the statistical software package “R” are an integral part of the course, which ensures that the students get hands-on experience of analyzing real world data.
Learning Objectives

Learning Objectives

  • Familiarize the students with advanced methods of econometric research in economics and finance.
  • A review of the classical linear regression model
  • Familiarize the students with advanced methods of econometric research in economics and finance.
  • Key panel data techniques
  • An overview of the matching models and regression discontinuity designs
  • Computer exercises using the statistical software package “Stata” are an integral part of the course, which ensures that the students get hands-on experience of analyzing real world data
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to apply the methods learnt when conducting own empirical analysis
  • Know key methods of econometric research, understand the causes and consequences of endogeneity, know the main methods for addressing this problem
  • Understand endogeneity as a key issue affecting causal inference; be able to critically examine existing research from this angle
  • Understand the limits of interpreting regression results in most settings (the ceteris paribus clause).
  • Be familiar with and be able to use key capabilities of the statistical package “R”, including its programming options
Course Contents

Course Contents

  • Overview of the classical linear regression model
  • Introduction to econometric package Stata
  • Endogeneity. Instrumental variables methods
  • Analysis of panel (longitudinal) data
  • Estimation of treatment effects. The difference-in-difference estimator
  • Propensity score matching and regression discontinuity models
Assessment Elements

Assessment Elements

  • non-blocking Work in the classroom
  • non-blocking Home assignments
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.4 * Home assignments + 0.35 * Exam + 0.25 * Work in the classroom
Bibliography

Bibliography

Recommended Core Bibliography

  • Bruce E. Hansen. (2013). Econometrics. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C0DB9E1E
  • Jeffrey M. Wooldridge. (2019). Introductory Econometrics: A Modern Approach, Edition 7. Cengage Learning.

Recommended Additional Bibliography

  • Kleiber, C., & Zeileis, A. (2008). Applied Econometrics with R. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=275761