The main objective of the course is to introduce students to structural estimation methods. While the course will be emphasizing the estimation of structural dynamic programming models, it will offer a link between other dynamic microeconometric models used in the Panel data literature and in the literature on duration and transition data. Some time will also be devoted to the estimation of structural behavioral econometric models, and in particular, those devoted to the estimation of preferences for risk and time.
At the end of the course, the student is expected to master a wide class of micro-econometric structural models. In particular, and in oder to pass the course, each student should be able to estimate an econometric model requiring specific programming using a software of their choice (R, Matlab, Stata, Fortran,….)
- Reduced-form dynamic discrete Choices
- Introduction to Duration Data analysis
- Discrete Stochastic Dynamic Programming Models (DSDPM). Introduction and definitions, the Bellman equation, introduction to recursive methods.
- An example of DSDPM. Solving a partial equilibrium search problem, maximum likelihood estimation.
- General Framework. State Space, Law of Motions, Classification of solution methods, Dimension of integrals, Curse of dimensionality.
- Direct Solution Methods. Schooling Decisions as an optimal stopping problem, Extreme value-dynamic Logit Model (Rust's method).
- Computationally Intensive Methods. Simulation Methods, Interpolation Methods.
- Identification and Estimation. A theorem by Hotz and Miller, the degree of underidentification, restrictions on preferences.
- Alternative Solution Methods. Estimation by Conditional Choice Probabilities, OLS estimation, Non rational expectations (Expectation Parametrization Method).
- The Estimation of Preferences for Risk and Time
Each week, a set of notes will be available on the course webpage (Pamplemousse). The lecture notes will be self-contained. However, for a review of dynamic programming concepts, the reader may consult the following
Bellman, R. (1957), Dynamic Programming, Princeton University Press, Princeton.
Eckstein, Z. et K. Wolpin (1989), "The Specification and Estimation of Dynamic Stochastic Discrete Choice Models", Journal of Human Resources, 24, 562-598.
Rust, J. (1994), "Structural Estimation of Markov Decision Processes" in Handbook of Econometrics, Engle, R. et D. McFadden (eds.), Elsevier Science, North-Holland Publishers, Amsterdam, 3081-4143.