Skip Navigation

Spring 2014 - 63770 - PA397C - Advanced Empirical Methods for Policy Analysis

Analyzing Government Economic Statistics

Instructor(s): Auerbach, Robert D.
Unique Number: 63770
Day & Time: Th 9:00 am -12:00 pm
Room: SRH 3.216/219
Waitlist Information:For LBJ Students: UT Waitlist Information
Course Overview

In addition to the Introduction to Empirical Methods course in the common core, MPAff students are required to take another three-hour course in quantitative analysis, selected from among a set of courses focusing on the application of quantitative theory and techniques to policy analysis. Topics offered vary from year to year but include econometrics, demographic techniques, systems analysis, simulation modeling, and quantitative indicator methods. As the second course in the two-course MPAff quantitative sequence, this course is intended to provide students with an in-depth understanding and hands-on experience with a specific quantitative method useful in policy analysis. This course is usually taken during the second semester of the first year.

Section Description

“Eureka, there’s a 98 percent correlation!” Many users of time series data do not realize that trends and common movements of observations of variables over time, such as employment and prices over seasons and business cycles, present special problems. Improper tests can find statistical correlations that do not indicate causality. Also, mutilation problems arise from a commonly used smoothing procedure: moving averages. This is a user-friendly course in applied statistics in which estimates of economic activity including national income estimates, employment survey data, and financial data will be described. Attempted solutions will be presented for transforming time series for testing purposes. The methods presented include Granger causality tests, autoregressive filter procedures, and cointegration procedures. Control problems that occur when a variable is under the control of the government will be addressed. There will be a final statistical project.