You should expect this schedule to be a dynamic document. Although the the objectives for this course are fixed, the details of how to accomplish those objectives will depend on the interaction between the instructors and students in the course.

When I make changes, these changes will be recorded in the history of this page on GitHub so that you can track what has changed. I will notify you of changes in class or via email.

I will ask the class for feedback frequently—both in class and anonymously via surveys. Please let me know what is working and what can be improved. I will make adjustments based on this feedback.

Week 1

  • Introduction
  • Install software and set up GitHub accounts

Reading

skim after class, I will discuss ideas inspired by these sources:

Agenda

For loops and functions

R markdown

Week 2

Readings

  • Chapters 1 and 2 of Wooldridge
  • Chapter 1 of Mostly Harmless Econometrics
  • Review matrix algebra: you should understand what vectors and matrices are. How to add, subtract, and multiply them. And what a matrix inverse is, what it’s properties are. You don’t need to worry about how to calculate a matrix inverse, since you’ll never do that by hand.

  • Wooldridge appendix
  • Kahn Academy course Matrices lessons: Introduction, Representing linear systems of equations, elementary matrix row operations, adding and subtracting matrices, multiplying matrices by scalars, properties of matrix addition and scalar multiplication, multiplying matrices by matrices, properties of matrix multiplication, introduction to matrix inverses.
  • Matrix algebra handout

Friday: paragraph proposal due.

Class Agenda

  1. Confirm that you can push to GitHub Accounts.
  2. Discuss Project Assignment 1
  3. Types of research questions, types of analysis. slides

Supplementary Reading

Class Agenda

  1. Assigned and started working on Assignment 1

Supplementary Reading

See Linear Regression Functions in R for more help

NO CLASS due to MPSA

Research Project Assignment 1 due at 17:00 PDT.

Week 3

No Class

Week 4

Topics

  • Omitted Variable Bias
  • Multiple regression anatomy
  • Properties of Ordinary Least Squares
  • Inference for Ordinary Least Squares
  • Interpretation of p-Values and Confidence Intervals
  • Substantive Significance
  • Power

Readings

Week 5

Topics

  • Non-constant Variance (Heteroskedasticity)
  • Omitted Variable Bias
  • Measurement Error
  • Multicollinearity
  • Outliers
  • Non-linearity
  • Interaction Terms

Readings

  • Wooldrige, Ch. 7: “Multiple Regression Analysis with Qualitative Information”
  • Wooldridge, Ch 8: “Heteroskedasticity”
  • Wooldridge, Ch 9.1 “Functional Form Misspecification”
  • Wooldridge, Ch 9.6 “Least Absolute Deviations Estimation”
  • Wooldridge, Ch 9.4 “Properties of OLS under Measurement Error”
  • Wooldridge, Ch 17.1: “Logit and Probit Models for Binary Response”
  • Brambor, Thomas, and William Roberts Clark, and Matt Golder. 2006. “Understanding Interaction Models: Improving Empirical AnalysesPolitical Analysis
  • Matt Golder Interactions

Due Project paper draft #1: Theory, Data Description, and Model Sections.

  • slides on overview of OLS assumptions and regression diagnostics.

Week 6

Due Peer Review of project draft #1.

Week 7

  • Model Selection
  • Model Fit
  • Prediction
  • Cross-Validation

Week 8

Topics

  • Experimental Ideal
  • Potential Outcomes
  • Selection on Observables: Regression

Readings

  • MHE, Ch 2: “The Experimental Ideal”
  • MHE, Ch 3: “Making Regression Make Sense”

Due Project draft analysis

Week 9

  • Fixed Effects
  • Differences-in-differences
  • Clustered Standard Errors

Readings

  • MHE, Ch 5: “Parallel Worlds”
  • MHE, Ch 8: “Non-standard Standard Error Issues”
  • Wooldridge, Ch 13: “Pooling Cross Sections Across Time”
  • Wooldridge, Ch 14: “Advanced Panel Data Methods”

Due Peer review of Project analysis

Week 10

Topics

  • Writing, presenting results

Readings

Due Draft of project final paper

Finals Period

Final paper due on