1. General Policies
   1. Grading
      1. Final Exam
      2. Groups
      3. Assignments
   2. Readings
2. Other Resources
   1. Friday Sections
   2. HowTos
3. Topics
   1. Multiple Equation Models
   2. Some Non-parametrics
   3. Causality & Correlation
   4. Identification & Instrumental Variables
   5. Generalized Method of Moments
   6. Discrete Choice & Maximum Likelihood
   7. Resampling & the Bootstrap
   8. Cross-Validation & Other Ideas from Machine Learning

For class-related conversations, please go to edstem.org.

Discussion of administrative issues: [video]

The final exam will be “take-home”, and be made available by 11am on Tuesday, May 9 (if there is a consensus that an earlier date is preferable then we can reschedule). You will have 24 hours to complete the final.

You should regard yourself as a member of a group, and your group will be responsible for working together on certain assignments. Group sizes should be between 3–6 (inclusive).

We will ask you to complete an assignment every 2–3 weeks. We expect you to work on these with others in your group. Your group should turn in one set of notes and proposed solutions. The problem sets will feature two different kinds of problems.

1. Exercises

   These are called exercises because they are meant more as tests of comprehension than as more difficult problems. Exercises will be graded on a “check”, “check +”, “check -” system.

   Not completing an exercise will be much worse for the grades of people in your group than receiving a “check -”.

2. Problems

   Problems are meant less as checks on comprehension and more the part of the assignment that involves more critical thinking. “Problems” may not have a “right” answer (though there will always be many wrong answers).

• “Hansen” refers to Bruce Hansen’s Econometrics Textbook. This was recently published by Princeton University Press, and belongs on your bookshelf.
• Other readings will be added topic by topic (usually these will be available electronically).

Lucy Hackett will hold weekly sections on Fridays. She’ll present material pertaining to both econometrics and programming.

It’s critical to note that we expect you to develop some fluency in the programming language python.

• GitHub: How to view, files, fork, edit, and issue pull requests (PRs). https://youtu.be/krDQk5ZzP8U
• GitHub to DataHub: How to use datahub to run & edit code from github: https://youtu.be/HBM8jgvYPtA
• Python Bootcamp: [datahub][github]

Discussion of topics for 2nd half of course: [video]