Fall 2012 - 62475 - PA397 - Introduction to Empirical Methods for Policy Analysis
|Instructor(s):|| Matwiczak, Kenneth
|Day & Time:|
|Waitlist Information:||For LBJ Students: UT Waitlist Information|
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 section of PA397 will be conducted as an “on-line” section of the course. There will be no regularly scheduled “in-person” meetings of this course. The course will be conducted “on line” using the electronic Blackboard system, and will include regularly scheduled instructor and teaching assistant “office hours” at set times during the week, assigned readings and graded homework problem sets, “take-home” exams, and an on line project presentation during the final class week of the Fall academic semester. All interactions with the course instructor and teaching assistant will be electronic or telephonic. Students enrolling in this section of PA397 are required to have “Skype” (video and verbal) capability on their personal computer.
The course develops basic competence and skills in problem solving and quantitative methods applied to public policy analysis. It emphasizes the art and skill of converting problem descriptions into quantitative models, and the analysis and interpretation of these models.
We will review basic concepts of probability, probability distributions, and descriptive statistics as a means of communicating and describing data of all types. Following a brief discussion of data sources and sampling methods, we study the use of sample data to make estimates of, and inferences about, descriptive parameters of larger populations. Using these inference skills and sample data, we develop and test linear regression models to describe relationships between a characteristic dependent variable (such as income) and one or more other explanatory variables (such as gender, age, etc.).
Following our statistical analysis “block” we look at how quantitative methods can be applied to support decision-making. We will model decisions using decision matrices and decision trees. With these tools, we evaluate decisions made under conditions of certainty, risk, and uncertainty, We also consider the value of obtaining additional information to support the decision analysis, and how to incorporate the decision maker's value judgments into the decision model. In all our decision analysis work we consider the sensitivity of the decision to changes in the problem conditions. After a brief look at making choices involving several different, diverse characteristics, (i.e. multi-criteria decisions), we introduce the idea of "mathematical optimization" as a way to search for the best possible solution to a decision problem which may have constrained or unconstrained decision parameters. This primarily involves using linear programming modeling and analysis techniques.
In general, the course is an applications-focused course rather than a rigorous theoretical or mathematical development. Emphasis is on the use and interpretation of quantitative modeling and analysis methods in policy evaluation and decision-making. Students are required to make extensive use of Microsoft Excel spreadsheets for homework assignments, applications exercises, and take-home exams. Students also participate in a “phased” semester project on a pseudo-real world problem of their own choosing, making an oral presentation on the results of their work at the end of the semester. Two “take-home” exams will complement the four or five graded homework problem sets.
- Successful completion of a "typical" college-level mathematics course, including algebra / linear algebra. Plus, one semester of introductory statistics, or equivalent. Calculus "validation."
- Familiarity with computer spreadsheets, or willingness to devote out of class time to become familiar with using spreadsheets such as MS-Excel. "Familiarity" includes recording data in a spreadsheet, performing simple calculations, and using the graphing functions.