Skip Navigation

Fall 2012 - 62487 - PA397 - Introduction to Empirical Methods for Policy Analysis

Instructor(s): North, Rebecca
Unique Number: 62487
Day & Time: M 2:00 pm -5:00 pm
Room: SRH 3.214
Waitlist Information:For LBJ Students: UT Waitlist Information
Final Exam Information:December 12, 2012 - 2:00pm - 5:00pm SRH 3.124
Course Overview

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.

Section Description
IEM is the first of a two-course sequence in quantitative methods in the core MPAff curriculum. This course is designed to develop quantitative analysis skills that can be applied to address public policy issues. Emphasis is placed on the conceptual reasoning underlying analytic methods, as well as the application and interpretation of various quantitative models. Emphasis is also placed on cultivating communication skills in the context of quantitative analysis. This course fosters an ability to apply the appropriate tool for a particular policy problem, to understand the limitations of the various approaches, and more broadly, to formulate views about the role of quantitative analysis in public policy.
This course is organized around three main topics: statistical inference, optimization, and decision-making. Specific topics include:
  • Examining data and understanding statistical inference
  • Modeling policy issues with linear regression models
  • Thinking about optimization in a policy context with linear and non-linear functions, and with constraints
  • Applying quantitative methods to decision-making in the context of uncertainty and risk
  • Developing views about the role of quantitative analysis in public policy