Fall 2014 - 62845 - PA397 - Introduction to Empirical Methods for Policy Analysis
|Instructor(s):|| Wilson, Robert H.
|Day & Time:||T 9:00 am -12:00 pm|
|Waitlist Information:||For LBJ Students: UT Waitlist Information|
|Final Exam Information:||December 10, 2014 - 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.
Quantitative methods provide the means to integrate empirical evidence into all phases of the public policy process. These methods are used in describing policy problems, analyzing policies and their impacts, and framing management decision-making. This course will develop an understanding of basic quantitative tools, their applications in public policy and management, and strategies to interpret and communicate empirical evidence to multiple audiences. The methods discussed include modeling, optimization, sensitivity analysis, probability theory, statistical inference, estimation, and prediction. These methods will be applied to constrained decision-making (including linear programming), probabilistic dimensions of policy choice (including the application of Bayesian techniques), statistical modeling of policy problems, and statistical forecasting. The course is taught in a problem-solving applications mode, with frequent computer applications, rather than a theoretical and mathematical approach. The limitations of quantitative methods are also addressed.
As the first course in the MPAff’s quantitative methods sequence, the emphasis is on broad exposure to techniques and developing skills through applications in homework assignments. It is highly recommended that this course be taken in the fall semester of the first year. The background prerequisites are college level algebra (including interpretation of graphs), matrix algebra, basic differential calculus, probability theory, and simple descriptive statistics.
Course requirements include homework assignments, two exams (Mid-term and Final exam) and a term project.