EC 425/525, Spring 2019
Welcome to Economics 425/525: Econometrics III (Spring 2019) at the University of Oregon (taught by Dr. Ed Rubin).
Lecture Monday and Wednesday 2:00pm–3:20pm, McKenzie Hall 240A
Lab Friday 12:00pm–12:50pm, Gerlinger Hall 302
- Ed Rubin Monday 4:00pm–5:00pm and Thursday 9:00am–10:00am, PLC 519
- Jenni Putz Monday 4:00pm–5:00pm and Friday 9:30am–11:00am, PLC 523
We will mainly use two books.
Mostly Harmless Econometrics: An Empiricist's Companion (MHE)
by Angrist and Pischke
Your new best friend. Read it.
by Cameron and Trivedi
Also very readable and accessible.
Runner up (the standard):
Econometric Analysis (Greene)
Encyclopedic resource for all (most?) of the questions MHE does not answer.
Note: The linked slides (below) are
.html files that will only work properly if you are connected to the internet. If you're going off grid (camping + metrics?), grab the PDFs. You'll miss out on gifs and interactive plots, but the equations will actually show up. I've removed the within-slide (incremental) pauses in the (no pause) slides.
- An introduction to empirical research via applied econometrics.
- R: Light introduction—objects, functions, and help.
- Neyman potential outcomes framework (Rubin causal model)
- Selection bias and experimental variation in treatment
- R: Object types/classes and package management.
- What's the big deal about least-squares (population) regression?
- What does the CEF tell us?
- How does least-squares regression relate to the CEF?
- How do we move from populations to samples?
- What matters for drawing basic statistical inferences about the population?
- How can we learn about inference from simulation?
- How do we run (parallelized) simulations in R?
- Saturated models
- When is regression causal?
- The conditional-independence assumption
- Omitted-variable bias
- Good and bad controls
- Matching estimators: Nearest neighbor and kernel
- Propensity-score methods: Regression control, treatment-effect heterogeneity, blocking, weighting, doubly robust
- General research designs
- Instrumental variables
- Two-stage least squares
- Heterogeneous treatment effects and the LATE
- Sharp regression discontinuities
- Fuzzy regression discontinuities
- Graphical analyses
- General inference
- Cluster-robust standard errors
- The bootstrap
- Permutation tests (Fisher)
- Randomization inference (Neyman-Pearson)
- Object types/classes/structures
- Package management
- Math and stat. in R
- Data frames
- Data work with
- Getting data into and out of R
- Other regressions, e.g.,
- General simulation strategies
- Simulating IV in finite samples
- Logical vectors and
2–4 problem sets combining econometric theory and R.
Building a research project/proposal.
Step 1: Research question (causal relationship of interest) and motivation.
Should be between 2 sentences and 2 paragraphs.
Due 15 April 2019.
Step 2: Short proposal
Due 30 May 2019
- Instrumental variables
- Regression discontinuity
- Hayashi's Econometrics
- Mastering 'Metrics (undergrad version of Mostly Harmless)
- Stock and Waston
- Wooldridge ("Baby")
- Wooldridge (Adult?)
- Grant McDermott's Data Science of Economists course
- DataCamp's Introduction to R
- R for Data Science
- Advanced R
- RStudio's listing of online resources
Metrics and R