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.
Lecture notes [I'll update the notes on Fall 2022]
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.
© Marcelo Coca Perraillon, 2021. No part of the materials available through the https://clas.ucdenver.edu/marcelo-perraillon/ site may be copied, photocopied, reproduced, translated or reduced to any electronic medium or machine-readable form, in whole or in part, without prior written consent of the author. Any other reproduction in any form without the permission of the author is prohibited. All materials contained on this site are protected by United States copyright law and may not be reproduced, distributed, transmitted, displayed, published or broadcast without the prior written permission of author.