Syllabus
Welcome to this course! Here you’ll find details on the syllabus.
Topics
Identification, Credible Inference, and Marschak’s Maxim
We formally define identification and discuss (via examples) what people really mean when they talk about identification and credible inference. We use the Generalized Roy Model to compare identification via functional form to nonparametric identification.
We introduce Marschak’s Maxim as a guide for doing empirical model-based research.
Reading
The two survey articles by Keane (2010) (link) and Angrist and Pischke (2010) (link) - although aging - provide two important perspectives on the issues of credible inference in economics. Low and Meghir (2017) provide a nice review of the advantages of the structural approach.
The original paper by Marschak (1953) may be of interest. Heckman and Vytlacil (2007) provide a nice discussion of Marschak’s Maxim in the context of policy evaluation. They introduce (Heckman and Vytlacil 2005; Carneiro, Heckman, and Vytlacil 2011) the Marginal Treatment Effect as a tool for thinking about quasi-experimental estimators and policy evaluation.
Extremum Estimators
We introduce the concept of an extremum estimator and discuss conditions under which this estimator has good asymptotic properties, with specific applications to maximum likelihood, minimum distance, and generalized method of moments estimators. We discuss optimal weighting of the relative efficiency properties of these estimators.
Reading
This section relies heavily on the Newey and McFadden (1994) chapter of the Handbook of Econometrics. Although not necessary, Hayashi (2011) provides a very thorough treatment of all of these estimators.
Simulation Methods
We introduce simulation methods for the estimation of structural models, including the Simulated Method of Moments, Indirect Inference, and the Bootstrap method for inference.
Reading
You may find the Horowitz (2001) handbook chapter useful. Cameron and Trivedi (2005) provide a useful discussion of simulation-based estimators in their textbook.
Panel Data Methods
We talk about individual heterogeneity and discuss the use of panel data for detecting individual heterogeneity in data.
Discrete Choice and Dynamic Discrete Choice
We review some of the formalities of discrete choice models and consider estimation of these models in the presence of dynamics.
Assessment
There will be 7 problem sets. Your best 5 of these 7 problem sets will be worth 20%. Hence, you can skip two if you want.
Here is the proposed timeline of due dates. Submissions must be made through Canvas as a notebook (e.g. jupyter or quarto) formatted to html with printed output.
| Assignment | Due Date |
|---|---|
| Assignment 1 | March 22 |
| Assignment 2 | March 29 |
| Assignment 3 | April 5 |
| Assignment 4 | April 12 |
| Assignment 5 | April 19 |
| Assignment 6 | April 26 |
| Assignment 7 | May 3 |
Office Hours
I will provide a link on Canvas to sign up for my weekly office hours.