Fall 2013 - 63865 - PA397 - Introduction to Empirical Methods for Policy Analysis
|Instructor(s):|| Stolp, Chandler
|Day & Time:||W 9:00 am -12:00 pm|
|Waitlist Information:||For LBJ Students: UT Waitlist Information|
|Final Exam Information:||December 11, 2013 - 9:00am - 12:00pm SRH 3.124|
This course helps students develop an understanding of how basic quantitative tools are used in policy analysis. The major concepts discussed include modeling, optimization, sensitivity analysis, statistical inference, estimation, and prediction. These concepts are covered in the context of applications such as constrained decisionmaking based on calculus and on linear programming; policy choices with probabilistic information; evaluating and updating information with Bayesian techniques; estimating the impact of policy factors using regression models; and practical methods for forecasting. As the first course in the quantitative sequence, the emphasis is on broad exposure of techniques and appreciation of their contributions as well as their limitations in policymaking. Students must have fulfilled prerequisites in college-level algebra, calculus, and statistics before enrolling in this course. It is usually taken during the fall semester of the first year.
This is the first of a two-course core sequence designed to develop tools and communications skills involving the application of quantitative methods to public policy analysis. All the sections of Empirical Methods for Policy Analysis are organized around three broad topics: Optimization, decisionmaking, and statistical modeling. Specific topics treated in this section include:
- The art and science of modeling
- Spreadsheet simulation, optimization, and statistics
- Mathematical programming models
- Decision modeling
- Risk and uncertainty
- Probability models
- Linear regression models
- Statistical inference: Weighing evidence & testing hypotheses
This course is similar to a traditional second-semester graduate course in statistics, yet goes beyond a traditional statistics course in several ways. One way it does this is by organizing the material around the topics of decisionmaking and optimization. Another is that it stresses the importance of sensitivity analysis over conventional statistical significance throughout. A model, theory, or approach that is less sensitive to changes in underlying assumptions is more robust and more believable than one that is not. A third way in which this course differs from a typical statistics course is the attention given to conceptual foundations of probability theory that go beyond the usual textbook treatment of repeated sampling.
This section of IEM assumes that you are familiar with basic differential calculus, the rudiments of probability, and simple descriptive statistics. It also assumes that you are comfortable with elementary algebra (ie, that at the very minimum you know how to interpret an intercept and a slope coefficient) and are capable of learning how to work with both summation notation and basic matrix algebra.