This is an intermediate level, Ph.D. course in Applied Econometrics. Topics to be studied include specification, estimation, and inference in the context of models that include then extend beyond the standard linear multiple regression framework. After a review of the linear model, we will develop the asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models. We will then turn to instrumental variables, maximum likelihood, generalized method of moments (GMM), and two step estimation methods. Inference techniques used in the linear regression framework such as t and F tests will be extended to include Wald, Lagrange multiplier and likelihood ratio and tests for nonnested hypotheses such as the Hausman specification test. Specific modelling frameworks will include the linear regression model and extensions to models for panel data, multiple equation models, and models for discrete choice. The discussion will include elements of the following topics:

The Paradigm of Econometrics
Classical Linear Regression Model. Part 1. Specification and Computation
Classical Linear Regression Model. Part 2. Statistical Inference in Finite Samples
Asymptotic Theory and Instrumental Variables Estimation
Nonlinear Regression Models
The Generalized Regression Model
Methods of Estimation

• Instrumental variables estimation
• Maximum likelihood estimation
• Generalized method of moments (GMM) estimation
• Two step estimation
• Estimation by simulation; models with unobserved heterogeneity
• Bayesian methods in econometrics
• Non and semiparametric estimators

Brief Survey of Applications, Models and Techniques

• Techniques for analyzing models wtih panel data
• Binary and other discrete choice models
• Time series models
• Models for counts
• Censoring, truncation and sample selection