This is the new version my Health Services Methods I class (starting in the 2020 Fall semester). This class is an introduction to regression models (linear, logistic, probit, GLMs) and research designs for observational data (regression adjustment/propensity scores, difference-in-difference, regression discontinuity, instrumental variables, longitudinal data). We will practice model interpretation until it becomes (almost) second nature. The old version of this class was an introduction to regression modeling.

Please note the copyright notice below. Most of the material on this site will be part of a book on statistics/econometric methods to be published by Cambridge University Press. Read more about it at perraillon.com. Happy to receive feedback.

Syllabus (2020)

Lecture notes

L1: Overview of the class

L2: Introduction to Stata and review of linear/OLS regression

L3: Causal inference

L4: Applied review of regression

L5: Regression adjustment and propensity scores (intro)

L6: Difference-in-difference models (intro)

L7: Regression discontinuity (intro)

L8: Maximum likelihood estimation (MLE)

L9: Marginal effects to interpret models

L11: GLM models and analysis of cost data

L12: Propensity scores and matching estimators

L13: Difference-in-difference designs (estimation)

L14: Regression discontinuity designs: rdrobust, sharp and fuzzy RDD, and intro to instrumental variables. This has a comparison of parametric with nonparametric rdrobust, which, given a bandidth h, it's really a kernel-weighted parametric method (see slides 59 and 60). The data-driven optimal bandwidth is the more interesting part.

 

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