Spring 2015 - 61432 - PA397C - Advanced Empirical Methods for Policy Analysis | LBJ School of Public Affairs | The University of Texas at Austin

Spring 2015 - 61432 - PA397C - Advanced Empirical Methods for Policy Analysis

Econometric Tools for Policy Issues

This class will provide a practical, hands‐on approach to quantitative analysis of the impacts of public policy using the powerful (and relatively user‐friendly) Stata statistical programming system. The approach taken will blend a concise summary of the theory behind different approaches to estimating the causal effects of policy, with the practical challenges of coding and estimating actual models using real world data sets. Students will be required to estimate real models using real data sets on a weekly basis, and report on their results in class (20% of grade). In addition, there will be two in‐class project presentations (30% of final grade) using pre‐selected, common datasets, and a final paper and project presentation (50% of final grade) modeling a policy issue using a dataset of the student’s own choice. MPAff and MGPS students are encouraged to select a domestic or global policy issue, and dataset, related to their major field of interest.

Prerequisite: Students should already have a basic understanding of how to use ordinary least squares to estimate a linear regression model, from a prior introductory statistics or econometrics course.

Topics covered in this class will include:

  1. Probability and Statistics Review, Intro to Stata Basics; Simulation Methods
  2. Linear Regression Models: Classic Approach and Extensions
  3. Linear Regression Models: Specification Issues and Causal Interpretations; Potential Outcome Framework and Treatment Effects
  4. Coping with Endogeneity: Instrumental Variables Methods
  5. Panel Data Models: Difference‐in‐Differences and Fixed Effects; Correlated Random Effects
  6. Matching Estimators and Regression Discontinuity Designs
  7. Censored Data and Selection Models
  8. Categorical and Count Outcomes: Interpreting Logit, Probit, and Poisson Models
  9. Big Data: Tools to Deal with Really Large Datasets Or Really Large Models 
M.P.Aff
MGPS
Class Schedule: 
T
6:00PM to 9:00PM
SRH 3.316/350