Identification And Estimation Of Local Average Treatment Effects
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Identification And Estimation Of Local Average Treatment Effects
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Author : Joshua D. Angrist
language : en
Publisher:
Release Date : 1995
Identification And Estimation Of Local Average Treatment Effects written by Joshua D. Angrist and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.
Identification And Estimation Of Local Average Treatment Effects
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Author : Joshua D. Angrist
language : en
Publisher:
Release Date : 2007
Identification And Estimation Of Local Average Treatment Effects written by Joshua D. Angrist and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.
Nonparametric Iv Estimation Of Local Average Treatment Effects With Covariates
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Author : Markus Frölich
language : en
Publisher:
Release Date : 2002
Nonparametric Iv Estimation Of Local Average Treatment Effects With Covariates written by Markus Frölich and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.
Average Treatment Effect Bounds With An Instrumental Variable Theory And Practice
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Author : Carlos A. Flores
language : en
Publisher: Springer
Release Date : 2018-09-29
Average Treatment Effect Bounds With An Instrumental Variable Theory And Practice written by Carlos A. Flores and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-29 with Business & Economics categories.
This book reviews recent approaches for partial identification of average treatment effects with instrumental variables in the program evaluation literature, including Manski’s bounds, bounds based on threshold crossing models, and bounds based on the Local Average Treatment Effect (LATE) framework. It compares these bounds across different sets of assumptions, surveys relevant methods to assess the validity of these assumptions, and discusses estimation and inference methods for the bounds. The book also reviews some empirical applications employing bounds in the program evaluation literature. It aims to bridge the gap between the econometric theory on which the different bounds are based and their empirical application to program evaluation.
Inference On Local Average Treatment Effects For Misclassified Treatment
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Author : Takahide Yanagi
language : en
Publisher:
Release Date : 2018
Inference On Local Average Treatment Effects For Misclassified Treatment written by Takahide Yanagi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
We develop point-identification for the local average treatment effect when the binary treatment contains a measurement error. The standard instrumental variable estimator is inconsistent for the parameter since the measurement error is non-classical by construction. We correct the problem by identifying the distribution of the measurement error based on the use of an exogenous variable that can even be a binary covariate. The moment conditions derived from the identification lead to generalized method of moments estimation with asymptotically valid inferences. Monte Carlo simulations and an empirical illustration demonstrate the usefulness of the proposed procedure.
Clustered Local Average Treatment Effects
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Author : Didier Nibbering
language : en
Publisher:
Release Date : 2022
Clustered Local Average Treatment Effects written by Didier Nibbering 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.
Multiple unordered treatments with a binary instrument for each treatment are common in policy evaluation. This multiple treatment setting allows for different types of changes in treatment status that are non-compliant with the activated instrument. Therefore, instrumental variable (IV) methods have to rely on strong assumptions on the subjects' behavior to identify local average treatment effects (LATEs). This paper introduces a new IV strategy that identifies an interpretable weighted average of LATEs under relaxed assumptions, in the presence of clusters with similar treatments. The clustered LATEs allow for shifts across treatment clusters that are consistent with preference updating, but render IV estimation of individual LATEs biased. The clustered LATEs are estimated by standard IV methods, and we provide an algorithm that estimates the treatment clusters. We empirically analyze the effect of fields of study on academic student progress, and find violations of the LATE assumptions in line with preference updating, clusters with similar fields, treatment effect heterogeneity across students, and significant differences in student progress due to fields of study.
Microeconometrics
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Author : Steven Durlauf
language : en
Publisher: Springer
Release Date : 2016-06-07
Microeconometrics written by Steven Durlauf and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-07 with Literary Criticism categories.
Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.
Identification Of Population Average Treatment Effects Using Nonlinear Instrumental Variables Estimators
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Author : Cole Garrett Chapman
language : en
Publisher:
Release Date : 2014
Identification Of Population Average Treatment Effects Using Nonlinear Instrumental Variables Estimators written by Cole Garrett Chapman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Instrumental variables (Statistics) categories.
Nonlinear two-stage residual inclusion (2SRI) estimators have become increasingly favored over traditional linear two-stage least squares (2SLS) methods for instrumental variables analysis of empirical models with inherently nonlinear dependent variables. Rising adoption of nonlinear 2SRI is largely attributable to simulation evidence showing that nonlinear 2SRI generates consistent estimates of population average treatment effects in nonlinear models, while 2SLS and nonlinear 2SPS do not. However, while it is believed that consistency of 2SRI for population average treatment effects is a general result, current evidence is limited to simulations performed under unique and restrictive settings with regards to treatment effect heterogeneity and conditions underlying treatment choices. This research contributes by describing existing simulation evidence and investigating the ability to generate absolute estimates of population average treatment effects (ATE) and local average treatment effects (LATE) using common IV estimators using Monte Carlo simulation methods across 10 alternative scenarios of treatment effect heterogeneity and sorting-on-the-gain. Additionally, estimates for the effect of ACE/ARBs on 1-year survival for Medicare beneficiaries with acute myocardial infarction are generated and compared across alternative linear and nonlinear IV estimators. Simulation results show that, while 2SLS generates unbiased and consistent estimates of LATE across all scenarios, nonlinear 2SRI generates unbiased estimates of ATE only under very restrictive settings. If marginal patients are unique in terms of treatment effectiveness, then nonlinear 2SRI cannot be expected to generate unbiased or consistent estimates of ATE unless all factors related to treatment effect heterogeneity are fully measured.
Treatment Effects With Censoring And Endogeneity
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Author : Brigham R. Frandsen
language : en
Publisher:
Release Date : 2014
Treatment Effects With Censoring And Endogeneity written by Brigham R. Frandsen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
This paper develops a nonparametric approach to identification and estimation of treatment effects in a setting where observed outcomes are censored and treatment status may be endogenous and have arbitrarily heterogeneous effects. Identification is based on an instrumental variable that satisfies the exclusion and monotonicity conditions standard in the local average treatment effects framework. The paper also proposes a censored quantile treatment effects estimator, derives its asymptotic distribution, and illustrates its performance using Monte Carlo simulations. An empirical application to a subsidized job training program finds that participation significantly and dramatically reduced the duration of jobless spells, especially at the right tail of the distribution.
Aspects Of Identification And Partial Identification Of Average Treatment Effect In Binary Outcome Models
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Author : Chuhui Li
language : en
Publisher:
Release Date : 2015
Aspects Of Identification And Partial Identification Of Average Treatment Effect In Binary Outcome Models written by Chuhui Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Average treatment effect (ATE) is a measure that is frequently used in empirical analysis for measuring the impact of a policy amendable treatment on an outcome variable. Identification and estimation of the ATE have been of concern in empirical studies, as individuals are often only observed for one of the two treatment states in non-experimental data and the selection of treatment is often endogenous. This thesis studies the identification and estimation of the ATE of a binary treatment variable on a binary outcome variable. It particularly focuses on the implication of recent theoretical developments in the literature of partial identification to the econometric estimation of policy relevant effects in empirical applications.The notion of partial identification relates to the idea that in certain situations such as limited observability, more than one data generating process (DGP), or model, can give rise to the same data set we observe; these models are said to be observationally equivalent. In such circumstances policy relevant measures such as the ATE can not be point identified. It is only possible to set identify the measure by estimating an identified set (or bound) for such measures where all values in the set are consistent with the data.The analysis in the thesis is divided to three parts. The first part assumes that data is generated from a particular DGP with an additive error and a parametric distribution. It is found that the bias in the ATE estimator arising from a mis-specified error distribution is not significantly large if we have reasonable sample size and IV strength, even though there may be more significant biases for the model coefficients estimators. We also show that under this regime, the ATE can still be estimated reasonably well even without the existence of instrumental variables (IVs), relying on the assumed functional form and sample size for identification. The main part of the analysis is carried out in the remaining chapters under the partial identification framework. Performances of the estimated ATE bounds from four different estimation methods are compared by using the Hausdorff distance and Euclidean distance. It is found that for all sample sizes in the simulation, the easy to implement parametric methods yield better estimates than nonparametric methods. The strength of IVs also plays an important role on the partial identification of the ATE. The width of the identified set drops as the instrument strength grows. If an extremely strong instrumental variable is available, we may be able to achieve point identification of the ATE (the upper bound and lower bound will overlap). The simulation results further confirms that estimators from parametric methods are robust with regard to instrument strength, while the nonparametric estimators will deviate significantly from the true when the instrument strength is relatively small. Finally the point identification and partial identification of the ATE are applied to a real world data set to study the impact of the private health insurance status on dental service utilisation in Australia.The analysis in the thesis shows that conventional empirical analysis assuming a bivariate probit model could be misleading by estimating a much smaller range for the policy effect. This thesis illustrates with practical applications how various bound analysis of the ATE can be carried out and can provide more robust estimates for policy effects under much broader assumptions for the DGP.