# Spring 2010 Course Description

#### Advanced Empirical Methods for Policy Analysis

**Section Title:** |
Applied Econometrics for Policy Analysis |

**Instructor(s):** |
Chandler Stolp |

**Course:** |
P A 397C - Advanced Empirical Methods for Policy Analysis
(previously Applied Quantitative Analysis II) |

**Unique Number:** |
62725 |

**Day & Time:** |
Wednesdays, 9:00 AM - 12:00 PM |

**Room:** |
SRH 3.122 |

**Waitlist Information:** | **For LBJ Students:** UT Waitlist Information |

**This course fulfills requirements for the following specialization(s):**

- Social and Economic Policy

**Description:** This section of AEM is designed for masters and PhD students who wish to polish their skills in linear regression and gain a deeper understanding of the foundations of statistical inference, including some of the key competing theoretical perspectives and controversies that dominate current thought (sampling theory, likelihood theory, Bayesian theory). The approach taken in this section is somewhat more conceptual than that found in other sections, but is complemented throughout by an emphasis on applied statistical practice, especially in environments with "messy" data and/or in which substantive theory is weak–all of which are hallmarks of statistical work in public policy. Major themes in the course include:

- Review of the logic of descriptive, exploratory, and inferential statistics
- Nonparametric versus parametric statistics
- The principles of sampling theory and quasi-experimental design
- Linear regression and econometric modeling
- Likelihood inference, likelihood functions, and information theory
- Bayesian inference: Explicitly accounting for uncertainty in underlying theory
- Grappling with the "Specification Problem" in statistical inference
- Qualitative response models (logit, probit) and other models with restrictions on the dependent variable (Tobit)
- Random coefficient and hierarchical linear models
- Time series analysis and forecasting (time permitting)

Students are assumed to have been exposed to linear regression at the graduate level and to be willing to learn to work with summation notation and matrix algebra. Most of the statistical work in the course will involve using the SAS and Stata statistical packages.Return to Spring 2010 Course Schedule