Journal ArticleDOI
Estimation of Limited Dependent Variable Models With Dummy Endogenous Regressors
Reads0
Chats0
TLDR
The authors argue that much of the difference between binary and nonnegative outcomes comes from a focus on structural parameters, such as index coefcients, instead of causal effects, and propose several simple strategies to accommodate binary endogenous regressors.Abstract:
Applied economists have long struggled with the question of how to accommodate binary endogenous regressors in models with binary and nonnegative outcomes. I argue here that much of the difculty with limited dependent variables comes from a focus on structural parameters, such as index coefcients, instead of causal effects. Once the object of estimation is taken to be the causal effect of treatment, several simple strategies are available. These include conventional two-stage least squares, multiplicative models for conditional means, linear approximation of nonlinear causal models, models for distribution effects, and quantile regression with an endogenous binary regressor. The estimation strategies discussed in the article are illustrated by using multiple births to estimate the effect of childbearing on employment status and hours of work.read more
Citations
More filters
Posted Content
Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects
TL;DR: In this paper, the authors investigated conditions sufficient for identification of average treatment effects using instrumental variables and showed that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect.
Journal ArticleDOI
Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It
TL;DR: This paper showed that logistic regression estimates do not behave like linear regression estimates in one important respect: they are affected by omitted variables, even when these variables are unrelated to the independent variables in the model.
Journal ArticleDOI
The Oregon Health Insurance Experiment: Evidence from the First Year*
Amy Finkelstein,Sarah Taubman,Bill J. Wright,Mira Bernstein,Jonathan Gruber,Joseph P. Newhouse,Heidi Allen,Katherine Baicker +7 more
TL;DR: In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid, and the lottery provided an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low income adults using a randomized controlled design as mentioned in this paper.
Journal ArticleDOI
Controlling for endogeneity with instrumental variables in strategic management research
TL;DR: In this article, the authors present a framework to understand how endogeneity arises and how to control for it with instrumental variables to estimate causal relations with observational data, using the Heckman two-step procedure and the STATA commands of the exposed tests and methods.
Journal ArticleDOI
Neighborhood Effects on the Long-Term Well-Being of Low-Income Adults
Jens Ludwig,Jens Ludwig,Greg J. Duncan,Lisa A. Gennetian,Lawrence F. Katz,Lawrence F. Katz,Ronald C. Kessler,Jeffrey R. Kling,Jeffrey R. Kling,Lisa Sanbonmatsu +9 more
TL;DR: Using data from Moving to Opportunity, a unique randomized housing mobility experiment, it is found that moving from a high-poverty to lower-p poverty neighborhood leads to long-term improvements in adult physical and mental health and subjective well-being, despite not affecting economic self-sufficiency.
References
More filters
Journal ArticleDOI
Estimating causal effects of treatments in randomized and nonrandomized studies.
TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
Journal ArticleDOI
Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme
TL;DR: This paper decompose the conventional measure of evaluation bias into several components and find that bias due to selection on unobservables, commonly called selection bias in econometrics, is empirically less important than other components, although it is still a sizeable fraction of the estimated programme impact.
Journal Article
Identification of Causal effects Using Instrumental Variables
TL;DR: In this paper, a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable.
Journal ArticleDOI
Identification of Causal Effects Using Instrumental Variables
TL;DR: It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.
Journal ArticleDOI
Matching As An Econometric Evaluation Estimator
TL;DR: In this article, a rigorous distribution theory for kernel-based matching is presented, and the method of matching is extended to more general conditions than the ones assumed in the statistical literature on the topic.