 Spring 2018 - 60990 - PA 397C - Advanced Empirical Methods for Policy Analysis | LBJ School of Public Affairs | The University of Texas at Austin

## Spring 2018 - 60990 - PA 397C - Advanced Empirical Methods for Policy Analysis

#### Applied Regression

This class will explore how various regression modeling techniques can be used to study practical problems and issues in the social sciences, public policy, and other fields. The course is intended to be interdisciplinary, and the content will be tailored to some degree to match the interests of the students.

For students in the LBJ School of Public Affairs, this class builds upon the basic statistical concepts presented in Introduction to Empirical Methods (IEM) and qualifies as the second course in the core sequence in quantitative analysis.

The focus of this class is on statistical modeling concepts and how to analyze different problems and data sets.  Little attention will be devoted to formal proofs and derivations.

The content of this course will be very similar to an introductory Econometrics course, but we will use less mathematics than is used in the Economics Department.

Course topics will include:

• A review of basic statistical concepts
• Dealing with uncertainty in research and policy problems
• The thought process behind setting up a statistical model
• Software (Excel and SAS will be used)
• Approaches to estimating relationships (e.g., least squares, likelihood estimation, MCMC simulation)
• Interpretation of regression statistics
• Hypothesis testing (from frequentist and Bayesian perspectives)
• Multivariate regression
• Variable selection, specification testing, and functional forms
• How to identify and address modeling problems (e.g., autocorrelation, multicollinearity, heteroskedasticity, omitted variables bias, misspecification, endogeneity bias)
• Dummy variables and qualitative variables
• Logit, Tobit, and Poisson models
• Basic index theory
• Time series approaches
• Spatial regression
• Forecasting techniques
• An introduction to Bayesian estimation
• Causality
• A brief introduction to more advanced approaches, including non-parametric regression and simulation techniques.

### For Requirements and Expectations

Students should already be familiar with basic statistical concepts and be able to read and understand simple mathematical notation.

We will avoid the use of linear algebra.  You will not need any prior background with SAS.

If you are enrolled in the LBJ School of Public Affairs, IEM is a prerequisite.

There will be five or six homework problems, in addition to a mid-term exam and a term paper.  The term paper will likely be a group project.