You can download the notes for the class:

Notes by methods Notes by example

- Linear models for cross-section (running example will be labor
supply)
- Clarify concepts such as data, model, identification, estimation and inference
- Study theats to identification (endogeneity, measurement error, simultaneity)
- Finite sample gaussian inference & asymptotic inference (central
limit theorem)

- Lab on labor supply
- Heckman (77) paper
- Extras: clustering of standard errors, multiple testing, weak IV

- Multinomial decisions (we will use transportation and monopolistic competition)
- Unobserved heterogeneity
- Revisit multinomial problem and IIA
- Random effect model and EM-algorithm
- Extras: introduction to Variational Auto Encoders VAE

- Topics in non-parametrics and high-dimensional econometrics
- model complexity and bias/variance trade-off
- many regressors (application to network of peers)
- non parametric regression

- Dynamic programing
- Measuring sorting in the labor market (optional, if time allows)
- incidental parameter problems
- Fixed-effect approach, bias correction
- Group fixed effect approach

You can submit your homework either as solo or as a group of two. To
communicate about your homework, create a channel in our slack group
with a name starting with `grp_`

and then with the initials
of the members of your group. For instance the TA (Arjun) and myself
(Thibaut) would be `grp_at`

. Then please add Arjun and myself
as members of that group. I recommend you make your channel private.
Submit the analytical parts directly to this slack group.

Computational homeworks should be submitted as a self contained R markdown file. Please submit the Rmd file as well as the generated html output.

- Homework 1 (computer) due date TBD.
- Homework 2 (computer) due date TBD.
- Homework 3 (computer) due date TBD.
- Homework 4 (computer) due date TBD.

- The project can be done in groups of three. The goal is for you to take an economic or policy question and to develop a DGP for that question as well as a method adapted to answering that question given data. The project should highlight the potential pitfalls of given methods.
- 1 page proposal is due on week 7 or 8. This should include a question and a DGP.
- final submission of the project will be the week after finals.

- The midterm will in week 6 or 7.

Overall grade will be 2/5 homeworks, 2/5 midterm, 1/5 project.

These are some books that can be helpful to dig into more details of
the topics covered in class. These books are **not
required** for the class.

- Microeconometrics: Methods and Applications: A. Colin Cameron, Pravin K. Trivedi
- Econometric Theory and Methods, Davidson and MacKinnon
- An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- All of Statistics: A Concise Course in Statistical Inference, Larry Wasserman