Fall 2010 - 61235 - PA397 - Introduction to Empirical Methods for Policy Analysis
|Instructor(s):|| Wong, Pat
|Day & Time:||MW 9:00 - 10:30 am|
|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.
Scope and Objectives: This course is an introduction to a wide range of basic quantitative tools. The emphasis of this section is to foster analytic thinking and communication skills through rigorous conceptual reasoning. The mathematical and statistical techniques acquired through this process do not constitute the primary objective of the course. Instead they are the anchoring stories and beneficial side-products.
Upon successful completion of this course, students will be prepared to take any section of Advanced Empirical Methods for Policy Analysis, which will focus on techniques within a specialized quantitative topic.
Structure of Content: This course is organized into seven modules:
- Learning the role of quantitative analysis through philosophy
- Thinking about policy choices through calculus
- Visualizing policy issues through linear programming
- Making decisions in environments of uncertainty and risk
- Making guesses with bivariate statistical information
- Structuring reality with multivariate regression models
Learning Experiences: All assignments in this course are based on conceptual reasoning and its application to practical problems. None is driven by data crunching or the use of software. These assignments include:
Problem sets involving group problem-solving about once every three weeks;
Individual-based quizzes given out approximately once every three Fridays;
Final exam with a written and an oral component.
Expectation: Satisfactory completion of the quantitative prerequisites at the LBJ School—one semester each of differential calculus and basic statistics—is required. Camp LBJ level preparation is recommended. A Thursday evening tutorial will be offered. Abstention from long-hand note-taking in class is requested.