Overview: This online course comprises 28 remote sessions taught twice a week on Saturdays and Sundays from 12:00PM through to 2:00PM. Pittsburgh time (London time minus 5 hours, except October 25-31 when it is minus four hours).
Requirements: Students who register are expected to attend the complete sequence. Those who fail to attend regularly may be denied access to the course.
Registration: This course is open to Ph.D. students or Ph.D. holders in Economics or related fields. A reference letter may be required to confirm appropriate prerequisite training. Registration closes October 17, 2020.
The course analyzes the structural estimation and testing of nonlinear models. We explore relationships between economic theory, identification, estimation and econometric practice. It develops structural approaches for analyzing large cross sectional and longitudinal data sets, by exploiting restrictions derived from the equilibrium dynamic outcomes in individual discrete choice optimization problems and non- cooperative games. We investigate empirical content, characterize identification, evaluate alternative estimators and testing procedures, as well as consider counterfactuals. It has six segments:
- The first segment gives a flavor of structural estimation, by show how some examples of economic models induce a data generating process that provides the basis for estimating the structure of the economic environment, critical for conducting counterfactual simulations. We analyze the estimation of preferences in a model of continuous choices in a competitive equilibrium with complete markets; we derive inequalities that are induced by an equilibrium in a limit order market; we quantify the importance of moral hazard in an optimal contracting model; and we introduce the estimation of dynamic discrete choice optimization models.
- Then we profile many estimators that have been used to summarize data. They can be placed into four categories: estimators for linear data generating processes, parametric nonlinear processes, plus nonparametric and semiparametric estimators.
- The rationale for the third segment of the course is that the exact distribution of most nonlinear estimators is intractable, explaining why we resort to large sample theory. We analyze several notions of convergence, present laws of large number and central limit theorems, derive the asymptotic distribution of several nonlinear estimators, and show how to conduct hypothesis tests.
- The second half of this course integrates a discussion of the identification of primitives or deep parameters in economic models with data generating process of the equilibrium. The first segment focuses on various kinds of auctions and contracts.
- Then we analyze dynamic discrete choice models in more depth; we derive a representation of the value function, prove identification under the conditional independence assumption, illustrate how various CCP estimators work, analyze the concept of finite dependence, as well as relax the conditional independence assumption.
- Lastly we apply structural estimation methodology to lifecycle models of labor economics and product innovation.
Course website: http://comlabgames.com/structuraleconometrics