MG-GY 9413 Quantitative Methods Seminar I

3 Credits

The introductory PhD-level course covers quantitative analysis. Topics include specification, estimation and inference in the context of models that start with the standard linear regression framework. After reviewing the classical linear model, students develop the asymptotic distribution theory necessary for analyzing generalized linear and nonlinear models. Students then analyze estimation methods such as instrumental variables, maximum likelihood, generalized method of moments (GMM) and others. Inference techniques used in the linear regression framework (such as t and F tests) is extended to Wald, Lagrange multiplier, likelihood ratio and other tests. Finally, the linear regression framework is extended to models for panel data, multiple equation models and models for discrete choice.

Prerequisite(s): Doctoral standing or instructor’s permission.
Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0