Matching Regression Discontinuity Difference In Differences And Beyond
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Matching Regression Discontinuity Difference In Differences And Beyond
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Author : Myoung-jae Lee
language : en
Publisher: Oxford University Press
Release Date : 2016-05-02
Matching Regression Discontinuity Difference In Differences And Beyond written by Myoung-jae Lee and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-02 with Business & Economics categories.
Myoung-jae Lee reviews the three most popular methods (and their extensions) in applied economics and other social sciences: matching, regression discontinuity, and difference in differences. This book introduces the underlying econometric and statistical ideas, shows what is identified and how the identified parameters are estimated, and illustrates how they are applied with real empirical examples. Lee emphasizes how to implement the three methods with data: data and programs are provided in a useful online appendix. All readers-theoretical econometricians/statisticians, applied economists/social-scientists and researchers/students-will find something useful in the book from different perspectives.
Complier Effect For Difference In Differences Ratio In Fuzzy Regression Discontinuity With Double Running Variables
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Author : Myoung-jae Lee
language : en
Publisher:
Release Date : 2019
Complier Effect For Difference In Differences Ratio In Fuzzy Regression Discontinuity With Double Running Variables written by Myoung-jae Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
We consider regression discontinuity (RD) design with two, not one, running/forcing variables for a single treatment. The main identified entity is a 'double-difference-based ratio', where the numerator is a 'local difference in differences (DD)' for the response, and the denominator is a local DD for the treatment. This generalizes the well-known ratio-form identified entity for RD with a single running variable. We then establish that the ratio is the effect on compliers at the cutoff, which also generalizes the complier interpretation in RD with a single running variable. We also briefly touch upon instrumental variable estimation for RD with two running variables.
Regression Discontinuity Designs
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Author : Juan Carlos Escanciano
language : en
Publisher: Emerald Group Publishing
Release Date : 2017-05-11
Regression Discontinuity Designs written by Juan Carlos Escanciano and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-11 with Business & Economics categories.
Volume 38 of Advances in Econometrics collects twelve innovative and thought-provoking contributions to the literature on Regression Discontinuity designs, covering a wide range of methodological and practical topics such as identification, interpretation, implementation, falsification testing, estimation and inference.
Mostly Harmless Econometrics
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Author : Joshua D. Angrist
language : en
Publisher: Princeton University Press
Release Date : 2009-01-04
Mostly Harmless Econometrics written by Joshua D. Angrist and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-01-04 with Business & Economics categories.
In addition to econometric essentials, this book covers important new extensions as well as how to get standard errors right. The authors explain why fancier econometric techniques are typically unnecessary and even dangerous.
How Can Comparison Groups Strengthen Regression Discontinuity Designs
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Author : Coady Wing
language : en
Publisher:
Release Date : 2011
How Can Comparison Groups Strengthen Regression Discontinuity Designs written by Coady Wing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
In this paper, the authors examine some of the ways that different types of non-equivalent comparison groups can be used to strengthen causal inferences based on regression discontinuity design (RDD). First, they consider a design that incorporates pre-test data on assignment scores and outcomes that were collected either before the treatment became available or before the practice of assigning treatments based on a cut-off score began. The idea is to use these pre-test data to establish a baseline estimate of the relationship between the outcome variable and the assignment variable. Second, they evaluate a design that incorporates data on the assignment scores and outcomes of a single contemporaneous comparison group of units that are always ineligible for treatment. Here the idea is to establish baseline differences in the relationship between outcomes and assignment scores that prevail in the RD group and the comparison group. Third, they consider how unit level and group level covariates might be used to form an optimal control group from a pool of several candidate control groups. They explore how various methods of matching and reweighting can be used to construct a control group in which the functional relationship between the outcome and the assignment score closely resembles the relationship that prevails in the RD sample below the assignment cut-off value. In all three cases, they evaluate the statistical and behavioral assumptions that are required or the comparison group augmented RDD to produce unbiased estimates of specific treatment effects of interest. They also compare the assumptions required to extrapolate from the cut-off subpopulation to other sub-populations in the augmented RDD with the assumptions required to extrapolate from standard RDD.
Regression Discontinuity Designs
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Author : Matias D. Cattaneo
language : en
Publisher:
Release Date : 2022
Regression Discontinuity Designs written by Matias D. Cattaneo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
The regression discontinuity (RD) design is one of the most widely used nonexperimental methods for causal inference and program evaluation. Over the last two decades, statistical and econometric methods for RD analysis have expanded and matured, and there is now a large number of methodological results for RD identification, estimation, inference, and validation. We offer a curated review of this methodological literature organized around the two most popular frameworks for the analysis and interpretation of RD designs: the continuity framework and the local randomization framework. For each framework, we discuss three main topics: (a) designs and parameters, focusing on different types of RD settings and treatment effects of interest; (b) estimation and inference, presenting the most popular methods based on local polynomial regression and methods for the analysis of experiments, as well as refinements, extensions, and alternatives; and (c) validation and falsification, summarizing an array of mostly empirical approaches to support the validity of RD designs in practice.
A Practical Introduction To Regression Discontinuity Designs
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Author : Matias D. Cattaneo
language : en
Publisher: Cambridge University Press
Release Date : 2020-02-13
A Practical Introduction To Regression Discontinuity Designs written by Matias D. Cattaneo and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-13 with Political Science categories.
In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolás Idrobo, and Rocıìo Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical Sharp RD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the second Element, the authors discuss practical and conceptual extensions to this basic RD setup.
The Sage Handbook Of Regression Analysis And Causal Inference
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Author : Henning Best
language : en
Publisher: SAGE
Release Date : 2013-12-20
The Sage Handbook Of Regression Analysis And Causal Inference written by Henning Best and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-20 with Social Science categories.
′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.
Micro Econometrics For Policy Program And Treatment Effects
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Author : Myoung-jae Lee
language : en
Publisher: Oxford University Press, USA
Release Date : 2005
Micro Econometrics For Policy Program And Treatment Effects written by Myoung-jae Lee and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Business & Economics categories.
In many disciplines of science it is vital to know the effect of a 'treatment' on a response variable of interest; the effect being known as the 'treatment effect'. Here, the treatment can be a drug, an education program or an economic policy, and the response variable can be an illness,academic achievement or GDP. Once the effect is found, it is possible to intervene to adjust the treatment and attain a desired level of the response variable.A basic way to measure the treatment effect is to compare two groups, one of which received the treatment and the other did not. If the two groups are homogenous in all aspects other than their treatment status, then the difference between their response outcomes is the desired treatment effect. Butif they differ in some aspects in addition to the treatment status, the difference in the response outcomes may be due to the combined influence of more than one factor. In non-experimental data where the treatment is not randomly assigned but self-selected, the subjects tend to differ in observedor unobserved characteristics. It is therefore imperative that the comparison be carried out with subjects similar in their characteristics. This book explains how this problem can be overcome so the attributable effect of the treatment can be found.This book brings to the fore recent advances in econometrics for treatment effects. The purpose of this book is to put together various economic treatments effect models in a coherent fashion, make it clear which can be parameters of interest, and show how they can be identified and estimated underweak assumptions. The emphasis throughout the book is on semi- and non-parametric estimation methods, but traditional parametric approaches are also discussed. This book is ideally suited to researchers and graduate students with a basic knowledge of econometrics.
Non Standard Parametric Statistical Inference
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Author : Russell Cheng
language : en
Publisher: Oxford University Press
Release Date : 2017
Non Standard Parametric Statistical Inference written by Russell Cheng and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Mathematics categories.
This book discusses the fitting of parametric statistical models to data samples. Emphasis is placed on: (i) how to recognize situations where the problem is non-standard when parameter estimates behave unusually, and (ii) the use of parametric bootstrap resampling methods in analyzing such problems. A frequentist likelihood-based viewpoint is adopted, for which there is a well-established and very practical theory. The standard situation is where certain widely applicable regularity conditions hold. However, there are many apparently innocuous situations where standard theory breaks down, sometimes spectacularly. Most of the departures from regularity are described geometrically, with only sufficient mathematical detail to clarify the non-standard nature of a problem and to allow formulation of practical solutions. The book is intended for anyone with a basic knowledge of statistical methods, as is typically covered in a university statistical inference course, wishing to understand or study how standard methodology might fail. Easy to understand statistical methods are presented which overcome these difficulties, and demonstrated by detailed examples drawn from real applications. Simple and practical model-building is an underlying theme. Parametric bootstrap resampling is used throughout for analyzing the properties of fitted models, illustrating its ease of implementation even in non-standard situations. Distributional properties are obtained numerically for estimators or statistics not previously considered in the literature because their theoretical distributional properties are too hard to obtain theoretically. Bootstrap results are presented mainly graphically in the book, providing an accessible demonstration of the sampling behaviour of estimators.