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Regular version of the site
Master 2019/2020

Econometrics of Program Evaluation

Type: Elective course (Economics: Research Programme)
Area of studies: Economics
When: 2 year, 1, 2 module
Mode of studies: offline
Instructors: Vitalijs Jascisens
Master’s programme: Academic Economics
Language: English
ECTS credits: 5

Course Syllabus

Abstract

Today we witness the explosion in the availability of high quality data: increasingly governments (and firms) around the world open their datasets to the general audience. Simultaneously, we also see a huge demand both in policy and academic circles for people who are able to answer causal questions using these new datasets. This course provides a training in “classic” research designs and additionally teaches students how to implement these methods using a high level computing language.
Learning Objectives

Learning Objectives

  • The course consists of three parts: 1. “Classic” research designs; 2. R programming; 3. Reading group. The main goals of this course are:
  • providing students with necessary skills to understand identification and inference challenges of research designs;
  • getting programming skills on a high level computing language R;
  • teaching students to evaluate modern empirical literature.
Expected Learning Outcomes

Expected Learning Outcomes

  • 1. Understand assumptions behind “classic” research designs;
  • 2.Be able to use various research design to solve real world problems;
  • 3. Read and evaluate modern empirical literature;
  • 4. Ability to work with information: to find, evaluate and use information from various sources, necessary to solve scientific and professional problems;
  • 5. Ability to do research, including problem analysis, setting goals and objectives, identifying the object and subject of research, choosing the means and methods of research, assessing its quality;
  • 6. Ability to collect and analyse the data;
  • 7. Able to solve problems in professional sphere based on analysis and synthesis;
  • 8. Capability to work in a team.
Course Contents

Course Contents

  • 1. Introduction to Causal Inference in Economics.
  • 2.Introduction to R programming.
  • 3. Vectorized Computation and Data Aggregation in R.
  • 4. Selection on Observables Research Design.
  • 5. Difference in Differences Research Design.
  • 6. Instrumental Variables Research Design.
  • 7. Bootstrap.
  • 8. Regression Discontinuity Research Design.
  • 9. Reading Group.
Assessment Elements

Assessment Elements

  • non-blocking Presentation of the paper
  • non-blocking Empirical project
    Done in groups of two students.
  • non-blocking Problem set 1
    Done in groups of two students.
  • non-blocking Problem set 2
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.25 * Empirical project + 0.25 * Presentation of the paper + 0.25 * Problem set 1 + 0.25 * Problem set 2
Bibliography

Bibliography

Recommended Core Bibliography

  • Angrist, J. D. (DE-588)124748430, (DE-576)166629405. (2009). Mostly harmless econometrics : an empiricist’s companion / Joshua D. Angrist and Jörn-Steffen Pischke. Princeton, NJ [u.a.]: Princeton Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.286816679

Recommended Additional Bibliography

  • Computer age statistical inference : algorithms, evidence, and data science, Efron, B., 2017
  • Field experiments : design, analysis, and interpretation, Gerber, A. S., 2012
  • Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, & Maintainer Trevor Hastie. (2013). Type Package Title Data for An Introduction to Statistical Learning with Applications in R Version 1.0. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.28D80286
  • Hands-On programming with R, Grolemund, G., 2014
  • Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9780521885881
  • Joshua D. Angrist, & Jörn-Steffen Pischke. (2014). Mastering ’Metrics: The Path from Cause to Effect. Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.pup.pbooks.10363
  • Lee, M. (2016). Matching, Regression Discontinuity, Difference in Differences, and Beyond. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780190258740