Armenian Independent University - Course Syllabus for Topics in Economics - Course I
Description: The main goal of this course is to introduce the students to topics and tools that are at the frontier of economic research. Course I, which will be taught in the Spring of 2022, will cover the main econometric/statistical tools used by economists to answer a wide range of economic questions. Course II, which will be taught in the Fall of 2022, will cover a range of topics in macroeconomics and finance.
Schedule: The course will meet once a week on Friday at 14:00-15:30 Yerevan time.
Course Plan
Feb 11: Welcome speech and Q/A session based on the syllabus and grading system. From 14:00-14:30.
Feb 11 and Feb 18: Nona Atoyan, AUA’19/YSMU/ Shirakatsy Lyceum (This lecture 14:30-15:30)
Introduction to Key Concepts: Plagiarism, Critical Thinking, Paraphrasing
Academic Writing
Feb 25 and Mar 4: Gegam Shagbazian, University of Padova
Data preparation in Stata for analysis (For those students who do not have experience working in Stata. The course is about how to transform, clean, and prepare raw data into an appropriate dataset to run the first regression.) Will be taught in Russian.
Mar 11 and Mar 18: Azamat Devonaev, University of Luxembourg
Introduction to OLS and endogeneity issues
Measurement errors
Mar 25 and Apr 1: Hayk Sargsyan, Universitat Pompeu Fabra
Introduction to Panel data models (FE, RE models, Hausman tests)
Dynamic Panel data models
Apr 8: Geghetsik Afunts, CERGE-EI, Charles University, The Czech Academy of Sciences
Instrumental Variables
Apr 29: Geghetsik Afunts, CERGE-EI, Charles University, The Czech Academy of Sciences
Differences-in-Differences
May 6 and May 13: Narine Yegoryan, Humboldt University Berlin
Preference elicitation using choice experiments
Introduction to choice-based conjoint analysis
Discrete choice models and market simulations with R
May 20 and 27: Anush Ghambaryan, HSE University, Ecole Normale Superieure - PSL University
Decision theory and Neuroscience: research questions and methods
Introduction to computational modeling in neuroeconomics
Grading: The students will be graded based on bi-weekly quizzes and a final assignment.
Bi-weekly quizzes (40%): After each class, students will get quizzes and/or small tasks to do.
Final Assignment (60%): Students will need to choose an academic article from a pool suggested by the lecturers, and then replicate its results using the tools learned in the course.
Final Grade = 40% Quizzes + 60% Final Assignment
Office hours: Please request appointments by e-mail
Recommended Literature:
Joshua D. Angrist and Jörn-Steffen Pischke “Mostly Harmless Econometrics: An Empiricist's Companion” Lecture notes
Verbeek, M. (2017). A Guide to Modern Econometrics (Vol. 5th edition). Hoboken, NJ: Wiley.
Recommended Additional Bibliography:
Greene, W. H. (2012). Econometric Analysis: International Edition : Global Edition (Vol. 7th ed., International ed). Boston: Pearson Education. Retrieved from
Härdle, W., Müller, M., Sperlich, S. A., & Werwatz, A. (2004). Nonparametric and Semiparametric Models. Switzerland, Europe: Springer. Retrieved from
Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing).Keele, L., & Wiley InterScience (Online service). (2008). Semiparametric Regression for the Social Sciences. Chichester, England: Wiley. Retrieved from
Koenker, R. (2005). Quantile Regression. Cambridge: Cambridge University Press.