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Regular version of the site
Master 2022/2023

Econometrics II (advanced level)

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Area of studies: Economics
When: 1 year, 3, 4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Dmitry Kislitsyn
Master’s programme: Applied Economics and Mathematical Methods
Language: English
ECTS credits: 6
Contact hours: 36

Course Syllabus

Abstract

This course will introduce concepts of causal inference. The experimental ideal, that is, random assignment of the treatment, is often impossible or impractical. Thus, we must look for alternative strategies that allow for causal identification when we do not have control over treatment assignment. In this course, we will explore the most common methods to identify causal effects in observational data. The aim is to help you to understand modern applied econometric methods and to foster the skills needed to plan and conduct your own science or business research projects.
Learning Objectives

Learning Objectives

  • Provide students with analytic and quantitative framework to implement causal inference
Expected Learning Outcomes

Expected Learning Outcomes

  • Understand potential outcome and directed acyclic graph approaches
  • Know the fundamental problem of causal inference
  • Understand the deference between causal identification and estimation
  • Understand the role of randomization
  • Conduct estimation and inference for average treatment effects in RCT
  • Understand the role of covariate adjustment in causal inference
  • Learn how to apply instrumental variable design in research
  • Understand the role of fixed effects in causal inference
  • Learn how to apply difference-in-difference design in research
  • Learn how to apply regression discontinuity design in research
  • Translating concepts between academia and industry (e.g. A/B testing). Practical challenges in designing and implementing causal inference at scale
Course Contents

Course Contents

  • Causation
  • Experimental ideal
  • Causal inference with linear regression
  • Instrumental variables
  • Fixed effects
  • Difference-in-difference
  • Regression discontinuity design
  • Causal Inference in Industry
Assessment Elements

Assessment Elements

  • non-blocking Group projects
    The task is for groups of 5-6 people.
  • non-blocking Quizzes
    Short quizzes in class. They will take from 10 to 15 minutes, depending on the complexity.
  • non-blocking Test
    The test is carried out in the classroom (remotely in case of switching to online learning), in writing, 80 minutes. The test is a closed book, closed notes. Using course materials is not allowed.
Interim Assessment

Interim Assessment

  • 2022/2023 4th module
    0.4 * Test + 0.35 * Group projects + 0.25 * Quizzes
Bibliography

Bibliography

Recommended Core Bibliography

  • Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics : An Empiricist’s Companion. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=329761

Recommended Additional Bibliography

  • Causality tests in econometrics : choice of causal variables, Sreenivasulu, B., 2013

Authors

  • KISLITSYN DMITRIY VIKTOROVICH