HERC: Econometrics Course
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Econometrics Course

Winter 2019 Econometrics with Observational Data Course

This course is intended to provide an introduction to econometric methods used to analyze data in health services research. Topics will include: linear regression; research design; propensity scores; instrumental variables; quasi-experiments and difference-in-differences; mixed effects modeling; regression discontinuity; specifying the regression model; limited dependent variables; and cost as the dependent variable. Course material will assume knowledge of basic probability and statistics and familiarity with linear regression. Lectures are held on Wednesdays, with each hourly session beginning at 11:00AM Pacific/2:00PM Eastern.
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Target audience: Researchers who would like an introduction to econometric methods for observational studies in health services research. Course material will assume knowledge of basic probability and statistics and familiarity with linear regression.

If you are unable to enroll in a HERC course, it may be possible to view a recorded archive of each lecture. Some archived course content is limited to VA employees, other content is available to a broader audience. Please see the HSR&D training page for details.

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2019 Econometrics Course
January 23, 2019
Econometrics Course: Introduction & Identification
Todd Wagner, Ph.D.

The objective of this class is to introduce participants to the course. We start by briefly describing a randomized trial and the leverage of experimental design to understand causation. We then transition into understanding causal pathways when experimentation isn't possible. We introduce the concept of endogeneity and walk participants through the elements of an equation, as these equations often come up in other classes. Finally, we discuss the five main assumptions underlying the classic linear model, setting the stage for future classes.

January 30, 2019
Research Design
Wei Yu, Ph.D.

This lecture will provide a conceptual framework for research design. We will review the linear regression model and define the concepts of exogeneity and endogeneity. We will then discuss three forms of endogeneity: omitted variable bias, sample selection, and simultaneous causality. The discussion will include examples and a brief overview of possible solutions.

February 6, 2019
Propensity Scores
Todd Wagner, Ph.D.

Understanding causation with observational data is often more dependent on what we don’t observe than what we do observe. Multivariate techniques can be very useful for understanding observed characteristics. Propensity scores have emerged over the past 20 years as another way to control for observables. We describe the concepts behind propensity scores and how they have been used (and misused) in practice. Finally, we work through an example using propensity scores.

February 13, 2019
Natural Experiments & Difference-in-Diffrences
Jean Yoon, Ph.D.

Natural experiments have been increasingly utilized by researchers in recent years. In this lecture, we will define what a natural experiment is and describe different types of natural experiments. We will also provide an overview of the difference-in-differences estimator and discuss how it can be used to evaluate treatment effects in natural experiments. Finally, we discuss potential threats to validity when evaluating natural experiments.

February 20, 2019
Regression Discontinuity
Liam Rose, Ph.D.

This lecture provides an introduction to regression discontinuity design. We will review seminal applications to gain a conceptual understanding of the benefits and limitations of this design, and how it can allow for causal inference. We will also review how to interpret estimates of RD designs and best practices for implementation.

February 27, 2019
Instrumental Variables
Wei Yu, Ph.D.

This lecture will provide an introduction to instrumental variables (IV) regression. We will discuss necessary conditions for valid instruments, the intuition for how and why IV regression works, examples, and limitations.

March 6, 2019
Fixed Effects & Random Effects
Josephine Jacobs, Ph.D.

TBA

March 13, 2019
Specifying the Regression Model
Ciaran Phibbs, Ph.D.

Standard introductions to the ordinary least square (OLS) model pay limited attention to the right hand side variables. Several strong assumptions are made about the independent variables, including linearity and independence, that don’t always hold in health applications. This lecture will address some of the common problems with right hand side variables, and introduce methods to test for them, and methods to correct these problems. Issues to be addressed include non-linearity and functional form, multicollinearity, clustering, and robust standard errors.

March 27, 2019
Limited Dependent Variables
Ciaran Phibbs, Ph.D.

The ordinary least squares (OLS) model is based on a continuous dependent variable. This lecture will introduce some of the methods available to treat other forms of dependent variables. Topics will include dichotomous (yes/no) outcomes, count data models, and choice models.

April 3, 2019
Cost as the Dependent Variable (Part I)
Josephine Jacobs, Ph.D.

Statistical analysis of health care cost is made difficult by two data problems. Some patients incur disproportionate costs, a statistical property called skewness. Other patients incur no cost at all; the distribution is truncated. As a result of these problems, it is rarely a good idea to analyze cost using the classic linear statistical model, ordinary least squares (OLS). Transforming cost by the taking its log results in a variable that is more normally distributed, allowing use of an OLS regression. The parameters from this regression have a natural interpretation as the proportionate effect of a unit change in the independent variable on cost. Care must be used when predicting costs from a model based on the log of costs. Log models have other limitations. The most important of these is that they should not be used when there are many zero cost observations in the data.

April 10, 2019
Cost as the Dependent Variable (Part II)
Josephine Jacobs, Ph.D.

Health care cost can be difficult to analyze. In addition to skewness and truncation, the variance in cost data may be correlated with one of the predictor (independent) variables, a problem call heteroscedasticity. As a result of these problems, Ordinary Least Squares regression models may generate biased regression parameters and inaccurate predictions. Generalized linear models (GLM) are an important alternative. A GLM includes a link function and a variance structure. These are identified using specific tests. Another alternative is a two-part model, which can be used to analyze data with many observations in which no cost was incurred. Non-parametric tests can be used to compare the cost incurred by two or more groups. Although they have the advantage of not requiring any assumptions about the statistical properties of the cost variable, they can be too conservative, and they do not allow the analyst to control for the effect of other factors.