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Course Syllabus

2022/2023

Advanced econometrics

Type: Optional course (faculty)
Delivered by: Department of Economics
When: 4 module
Open to: students of one campus
Instructors: Louisa Selivanovskikh
Language: English
ECTS credits: 3
Contact hours: 36

Course Syllabus

Full Syllabus

Abstract

This course is designed for advanced bachelor students in Economics. It offers a broad introduction to essential quantitative research methods, with extensions and example applications from a selection of studies published in leading journals. The topics covered in the course include: statistical estimation and inference; measurement scales; principal components and factor analyses; regression, mediation and moderation; causality vs. association and methods that can be used to estimate causal relationships in the data. Computer exercises using statistical software package “Stata” are an integral part of the course. Students may also use “R”, if they so wish, provided they supply data and scripts that will allow replication of the results of their research projects.
Learning Objectives

Learning Objectives

  • • Be familiar with key methods of (micro-)econometric research, especially factor and principle component analyses, mediation and moderation in regression models, and experiments, • Be able to apply the methods learnt when conducting own empirical research, • Be familiar with and be able to use key capabilities of statistical packages “Stata” or “R”
Expected Learning Outcomes

Expected Learning Outcomes

  • Understand fundamental concepts of Linear regression analysis.
  • Know the principle and basic approaches to statistical evaluation and conclusions.
  • Understand and be able to analyze illustrations with stochastic modeling
  • Be able to perform deep regression analysis.
  • Be able to conduct a causal assessment.
  • Ability to conduct research work
Course Contents

Course Contents

  • 1. The principle and main approaches to statistical estimation and inference. Illustrations with stochastic simulation
  • 2. Deeper regression analysis: mediation and moderation.
  • 3. Estimating causal relations: The problem of omitted variable bias and approaches to dealing with it
  • 4. Experiments: Basics, estimation of experimental treatment effects, dynamic treatment effects. Research seminar
  • 5. Nonlinear regression, maximum likelihood estimation. Research seminar.
  • 6. Measurement scales, their applications and properties. PCA and factor analyses
Assessment Elements

Assessment Elements

  • non-blocking Other – problem sets
  • non-blocking In-class Participation
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2022/2023 учебный год 4 модуль
    0.1 * Other – problem sets + 0.8 * Exam + 0.1 * In-class Participation
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

Recommended Additional Bibliography

  • Bruce E. Hansen. (2017). Time series econometrics for the 21st century. The Journal of Economic Education, (3), 137. https://doi.org/10.1080/00220485.2017.1320610