syllabus.yaml

code: ECBS 6233
title: Empirical Research Methods
instructors:
    -   name: Miklós Koren
        bio: Miklós Koren is professor of economics at CEU, senior research fellow at the Institute of Economics, and research fellow of the Centre for Economic Policy Research. His research focuses on how talent and technology jointly determine business success. Professor Koren has more than two decades of experience with microeconometrics and quantitative macro modeling. He is a certified Carpentries Instructor.
        email: korenm@ceu.edu    
        office: QS B510 (Vienna)
credits:
    us: 1
    ects: 2
module:  
term: Fall
level: 
    - PhD
prerequisites:
    - At least one PhD-level applied microeconomics course. Can be take concurrently with this course.
description:
    content: The course introduces tools for reproducible research computing and efficient collaboration. We will learn to use plain text and the command line, version control, write modular and readable code. 
    relevance: All empirical work involves significant amounts of computer coding. The time invested in coding is best leveraged if the code can be reused by coauthors, by our future selves and future readers. 
learning_outcomes:
    - Understand folder structure. Perform operations in the command line on files in different folders.
    - Use a modular folder and file structure for empirical projects.
    - Automate repeating tasks with Stata scripts, using for loops and programs.
    - Read and write data in various formats in Stata. Explore data and fix common data quality errors.
    - Filter, aggregate, reshape, and combine data for analysis.
    - Automate and parametrize model estimation.
    - Combine different data cleaning and estimation tasks using Makefiles.
learning_areas:
    - type: Critical thinking
      outcomes:
        - Evaluate and compare different computing tools and methods.
    - type: Quantitative reasoning
      outcomes:
        - Explore and analyze a large number of observations from potentially many different sources. 
    - type: Technology skills
      outcomes:
        - Write short programs in shell, make and Stata.
        - Install and use packages in Stata.
        - "Use key programming tools in Stata: programs, loops."
    - type: Interpersonal communication skills
      outcomes:
        - Convey technical concepts verbally.
        - Collaborate with others on technical tasks.
    - type: Management knowledge and skills
      outcomes:
        - Create software with many components.
        - Organize work components effectively.
        - Meet deadlines.
    - type: Cultural sensitivity and diversity
      outcomes:
        - Work together with students of different backgrounds.
    - type: Ethics and social responsibility
      outcomes:
        - Understand ethical and legal constraints of acquiring data.
        - Apply good practices of data protection.
readings:
    required:
        - "Paarsch and Golyaev, 2016. A Gentle Introduction to Effective Computing in Quantitative Research: What Every Research Assistant Should Know. MIT Press. (PG henceforth)"
        - Koren, Miklós, Arieda Muço and András Vereckei, 2019a. “Introduction to the Command Line for Economics [website].” The Carpentries. https://datacarpentry.org/shell-economics/ (KMV_shell henceforth)
        - Koren, Miklós, Arieda Muço and András Vereckei, 2019a. “Economics with Stata [website].” The Carpentries. https://datacarpentry.org/stata-economics/ (KMV_Stata henceforth)
    recommended:
        - "Sargent, Thomas J. and John Stachurski. 2020. Python Programming for Economics and Finance [website], https://python-programming.quantecon.org/index_toc.html"
        - "Gentzkow, Matthew and Jesse M. Shapiro. 2014. Code and Data for the Social Sciences. https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf"
teaching_methods:
    - This is a participatory, experiential course. Students participate in live coding together with the instructor. This format facilitates quick and frequent feedback and enables students to achieve mastery in their computing skills.
assessments:
    -   description: Class participation
        weight: 40
    -   description: Take-home coding assignment
        weight: 60
technical_requirements:
    - Personal laptop computer with administrative privileges to install open source software.
    - "Operating system: Windows 10+ or Mac OS X 10.8+, or Linux 2.6.18+"
    - git bash (Windows only), https://gitforwindows.org/
    - Stata 16, free teaching licenses available upon request.
    - Internet access. 
outline:
    -   session: 1
        topic: Introduction to the command line
        reading: PG 2.1-2.3, KMV_shell, Episodes 1-3. 
    -   session: 2
        topic: Reading and writing files in different formats, Data cleaning in Stata
        reading: PG 4.8, KMV_Stata, Episodes 1-2
    -   session: 3
        topic: Save and Reuse your Work in .do Files
        reading: KMV_Stata, Episode 5
    -   session: 4
        topic: Repeat tasks with for loops
        reading: KMV_Stata, Episode 6
    -   session: 5
        topic: Finding and installing user-written Stata packages
        reading: ""
    -   session: 6
        topic: Writing your own Stata packages
        reading: ""