PUBPOL 5310

Applied Econometrics for Public Policy

Course Description

Course information provided by the 2025-2026 Catalog.

This class is an applied introduction to multivariate statistical inference that is aimed at graduate students with little prior statistical experience, and satisfies the Quantitative Methods and Analytics requirement in the MPA Program. We will begin with a brief introduction to basic statistical concepts and probability theory before introducing the linear regression model. We then review several tools for diagnosing violations of statistical assumptions, including how to deal with outliers, missing data, omitted variables, and weighting. We will next consider situations in which linear regression will yield biased estimates of the population parameters of interest, with particular attention paid to measurement error, selection on unobservables, and omitted variables. The course will end with an introduction to extensions of the linear regression model, including models for binary and categorical outcomes. While statistical modeling is the focus of the course, we proceed with the assumption that models are only as good as the theoretical and substantive knowledge behind them. Thus, in covering the technical material, we will spend considerable time discussing the link between substantive knowledge and statistical practice. The course is designed primarily for professional masters students.

Enrollment Priority REF-FA25 Enrollment preference given to: Master of Public Administration (MPA) students.

Last 4 terms offered (None)

Outcomes REF-FA25

• Conduct statistical analysis using the multiple regression tool.
• Diagnosis and understand the limitations to the multiple regression tool.
• Interpret results from a linear model.
• Critically assess statistical models displayed in scholarly research articles.
• Produce a data and research project using multivariate tools.

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