Coding for Applied Economists

Course Introduction

Notes

Welcome

  • Introduce yourself
  • Have students introduce themselves

Course goals

  1. Introduce students to coding languages used in economic analysis.
  2. Familiarize students with a specific language.
  3. Apply that language to answer quantitative economic research questions.
  4. Prepare students for job market and graduate school applications.

Expected background

  • Assumes no background in Stata or any of the other things we’ll learn
  • Assumes a familiarity with statistics (AAE 339 or equivalent)

Teaching methods

  • You don’t learn how ride a bike just by listening to someone talk about it and the same is true for computing
  • So we use a fair bit of class time for working on exercises, asking questions, and getting help
  • But just trying to learn the concepts behind computing is also tricky, so we don’t use a fully flipped class approach
  • I do, we do, you do
    • I will demo an idea using live coding
    • We will work on an associated exercise together in class
    • You will work on additional exercises on your own (both in & out of class)
  • Peer instruction
    • Interact with each other during the we do and you do parts
    • You are at least as likely to learn from your peers as from me
    • If you already know some of the material and are getting ahead, see if there’s someone you can help

Course structure

Open website

  • Lessons integrated with weekly assignments
  • Practice is really important for learning skills like this
  • All exercises are part of the assignments
  • Work on them until you get stuck and then ask for help, don’t struggle for hours on online help sites
  • Assignments for each of the 15 weeks of class
  • Assignments due 11:59 the day before class (Thursday night)
  • Final exam in which you will be given buggy and inefficient code that you will need to debug and improve upon
  • Graduate students will (in groups) replicate a published paper
  • I encourage you to keep up with deadlines whenever possible. Each step of the course builds on the last step and students who get behind often have a hard time catching up.
  • That said, life happens, so there is an automatic 48 hour extension if you need it. You don’t need to ask, you can just turn things in up to 48 hours late. While I will grade late assignments, I will not provide any feedback on the assignment. So if you want feedback on your work, you need to turn it in on time.
  • If an assignment turned in after the 48 hour grace period, the grade on the assignment will be reduced by 10% of its earned value for every day it is late including weekends.
  • If you need a longer extension just ask. I am happy to work with you on accomidations, but you need to ask prior to the due date.

Website

Walk through the website showing the following items

  • Schedule
    • Walk through schedule (quickly)
  • Readings
    • Read before relevant class
    • No need to do any challenges or exercises in readings
  • Lessons
    • Alternating short videos (to introduce idea) and exercise (to start using that idea)
    • Alternating between video and exercise will be most effective for most folks
  • Lectures
    • Lecture notes used in class
    • Not expected to read in advance; may be useful for review
    • May not match lecture precisely
  • Assignments
    • Submitted via Github or Overleaf

Syllabus

  • Grading
    • Code runs, uses the requested approach, and produces approximately the correct answer: 100%
    • Attempts to solve the problem and makes some progress using the core concept, but does not produce approximately the correct answer or demonstrate basic comfort with the core concept: 50%
    • Not submitted or no meaningful effort demonstrated: 0%
  • Challenge exercises
  • Give it a read and let me know if you have any questions

Office Hours

  • Tuesdays, 1:00-2:00 pm MST, Thursdays, 1:00-2:00 pm MST, Fridays, 10:00-11:00 am MST
  • In-person in ENR2 N215, or on Zoom (most Slack me if you want to meet on Zoom)

Slack, Github, and Overleaf

  • All communications should go through Slack, either via a channel or a DM to me
  • All assignments will be published on Github
  • Will publish modules as soon as the material is ready to be worked on
  • First thing Monday at the latest, but hopefully much earlier
  • All assignments will be submitted through Github
    • To submit, push commits
    • Then create an issue on the class repo
    • Assignments must be tagged with submission when you want to submit the assignment for grading
    • If you are submitting late, the assignment must be tagged with late
    • If you are submitting a challenge exercise, you must tab it with challenge
    • Failure to tag assignments will results in a reduction by 10% of the assignment’s earned value
  • Starting with Assignment 10, submissions will need to be made in both Github and Overleaf
  • We will only use Brightspace to record grades

Inclusive and Accessible Course

  • Learning computing is hard
  • Learning computing in a classroom that doesn’t provide a supportive environment is awful
  • It’s really important to me that this course provide a positive, inclusive, accessible, learning environment for everyone
  • If that is not the experience you are having for any reason, just let me know and I’ll actively work to correct it

First day demo

  • With the time we have left I will introduce Github and Stata

Summary

  • This course introduces coding languages for economic analysis.
  • Our goal is to prepare for quantitative economic research.
  • We assume familiarity with basic statistics but no coding background.