Skip Navigation

Spring 2011 - 62255 - PA397C - Advanced Empirical Methods for Policy Analysis

Econometrics for Policy Analysis

Instructor(s): Stolp, Chandler
Unique Number: 62255
Day & Time: W 9:00 - 12:00 pm
Room: SRH 3.122
Waitlist Information:For LBJ Students: UT Waitlist Information
Final Exam Information:May 11, 2011 - 9:00am - 12:00pm SRH 3.124
Course Overview

In addition to the Introduction to Quantitative Analysis course in the common core, students are required to take another three-hour course in quantitative analysis, selected from among a set of courses focusing on the application of quantitative theory and techniques to policy analysis. Topics offered vary from year to year but include econometrics, demographic techniques, systems analysis, simulation modeling, and quantitative indicator methods. As the second course in the two-course quantitative sequence, this course is intended to provide students with an in-depth understanding and hands-on experience with a specific quantitative method useful in policy analysis. This course is usually taken during the second semester of the first year. 

Section Description

This section of AEM is designed for masters and PhD students who wish to polish their skills in linear regression and gain a deeper understanding of the foundations of statistical inference, including some of the key competing theoretical perspectives and controversies that dominate current thought (sampling theory, likelihood theory, Bayesian theory). The approach taken in this section is somewhat more conceptual than that found in other sections, but is complemented throughout by an emphasis on applied statistical practice, especially in environments with "messy" data and/or in which substantive theory is weak–all of which are hallmarks of statistical work in public policy. Major themes in the course include:

  • Review of the logic of descriptive, exploratory, and inferential statistics
  • Nonparametric versus parametric statistics
  • The principles of sampling theory and quasi-experimental design
  • Linear regression and econometric modeling
  • Likelihood inference, likelihood functions, and information theory
  • Bayesian inference: Explicitly accounting for uncertainty in underlying theory
  • Grappling with the "Specification Problem" in statistical inference
  • Qualitative response models (logit, probit) and other models with restrictions on the dependent variable (Tobit)
  • Random coefficient and hierarchical linear models
  • Time series analysis and forecasting (time permitting)

Students are assumed to have been exposed to linear regression at the graduate level and to be willing to learn to work with summation notation and matrix algebra. Most of the statistical work in the course will involve using the SAS and Stata statistical packages.