Identification In Nonparametric Models For Dynamic Treatment Effects
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Identification In Nonparametric Models For Dynamic Treatment Effects
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Author : Sukjin Han
language : en
Publisher:
Release Date : 2018
Identification In Nonparametric Models For Dynamic Treatment Effects written by Sukjin Han 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.
This paper develops a nonparametric model that represents how sequences of outcomes and treatment choices influence one another in a dynamic manner. In this setting, we are interested in identifying the average outcome for individuals in each period, had a particular treatment sequence been assigned. The identification of this quantity allows us to identify the average treatment effects (ATE's) and the ATE's on transitions, as well as the optimal treatment regimes, namely, the regimes that maximize the (weighted) sum of the average potential outcomes, possibly less the cost of the treatments. The main contribution of this paper is to relax the sequential randomization assumption widely used in the biostatistics literature by introducing a flexible choice-theoretic framework for a sequence of endogenous treatments. We show that the parameters of interest are identified under each period's two-way exclusion restriction, i.e., with instruments excluded from the outcome-determining process and other exogenous variables excluded from the treatment-selection process. We also consider partial identification in the case where the latter variables are not available. Lastly, we extend our results to a setting where treatments do not appear in every period.
Impact Evaluation Treatment Effects And Causal Analysis Basic Definitions Assumptions And Randomised Experiments 2 An Introduction To Nonparametric Identification And Estimation 3 Selection On Observables Matching Regression And Propensity Score Estimators 4 Selection On Unobservables Nonparametric Iv And Structural Equation Approaches 5 Difference In Differences Estimation Selection On Observables And Unobservables 6 Regression Discontinuity Design 7 Distributional Policy Analysis And Quantile Treatment Effects 8 Dynamic Treatment Evaluation
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Author : Markus Fröhlich
language : en
Publisher:
Release Date : 2019
Impact Evaluation Treatment Effects And Causal Analysis Basic Definitions Assumptions And Randomised Experiments 2 An Introduction To Nonparametric Identification And Estimation 3 Selection On Observables Matching Regression And Propensity Score Estimators 4 Selection On Unobservables Nonparametric Iv And Structural Equation Approaches 5 Difference In Differences Estimation Selection On Observables And Unobservables 6 Regression Discontinuity Design 7 Distributional Policy Analysis And Quantile Treatment Effects 8 Dynamic Treatment Evaluation written by Markus Fröhlich and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Econometrics categories.
"This book on advanced econometrics is intended to familiarise the reader with technical developments in the area of econometric which is known under the label treatment e ect estimation, or impact or policy evaluation. In this book we have tried to combine the intuitive reasoning for identi cation and estimation with the econometric and statistical rigorousness. This holds especially for the complete list of stochastic assumptions and their implications in practise. Moreover, for both, identi cation and estimation we focus mostly on nonparametric methods (i.e. our methods are not based on speci c pre-speci ed models or functional forms) in order to provide methods that are quite generally valid. Graphs and a number examples of evaluation studies are applied to explain how sources of exogenous variation can be explored for disentangling causality from correlation"--
Identification Of Dynamic Treatment Effects By Instrumental Variables
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Author : Ruth Miquel
language : en
Publisher:
Release Date : 2002
Identification Of Dynamic Treatment Effects By Instrumental Variables written by Ruth Miquel 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.
Essays On Nonparametric And Semiparametric Identification And Estimation
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Author : Shenshen Yang
language : en
Publisher:
Release Date : 2021
Essays On Nonparametric And Semiparametric Identification And Estimation written by Shenshen Yang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This dissertation consists of three chapters in econometric theory, with a focus on identification and estimation of treatment effect in semi-parametric and nonparametric models, when there exists endogeneity problem. These methods are applied on policy and program evaluation in health and labor economics. \indent In the first chapter, I examine the common problem of multiple missing variables, which we refer to as multiple missingness, with non-monotone missing pattern and is usually caused by sub-sampling and a combination of different data sets. One example of this is missingness in both the endogenous treatment and outcome when two variables are collected via different stages of follow-up surveys. Two types of dependence assumptions for multiple missingness are proposed to identify the missing mechanism. The identified missing mechanisms are used later in an Augmented Inverse Propensity Weighted moment function, based on which a two-step semiparametric GMM estimator of the coefficients in the primary model is proposed. This estimator is consistent and more efficient than the previously used estimation methods because it includes incomplete observations. We demonstrate that robustness and asymptotic variances differ under two sets of identification assumptions, and we determine sufficient conditions when the proposed estimator can achieve the semiparametric efficiency bound. This method is applied to the Oregon Health Insurance Experiment and shows the significant effects of enrolling in the Oregon Health Plan on improving health-related outcomes and reducing out-of-pocket costs for medical care. The method proposed here provides unbiased and more efficient estimates. There is evidence that simply dropping the incomplete data creates downward biases for some of the chosen outcome variables. Moreover, the estimator proposed in this paper reduced standard errors by 6-24% of the estimated effects of the Oregon Health Plan. \indent The second chapter is a joint work with Sukjin Han. In this chapter, we consider how to extrapolate the general local treatment effect in a non-parametric setting, with endogenous self-selection problem and lack of external validity. For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to the policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This chapter investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables are only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to incorporate a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services. \indent In the third chapter, I investigate the partial identification bound for treatment effect in a dynamic setting. First, I develop the sharp partial identification bounds of dynamic treatment effect on conditional transition probabilities when the treatment is randomly assigned. Then I relax the randomization assumption and gives partial identification bounds, under a conditional mean independence assumption. Using MTR and MTS assumptions, this bound is further tightened. These bounds are used on estimating labor market return of college degree in a long term, with data from NLSY79
Identification Of Treatment Effects Using Control Functions In Models With Continuous Endogenous Treatment And Heterogeneous Effects
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Author : Jean-Pierre Florens
language : en
Publisher:
Release Date : 2008
Identification Of Treatment Effects Using Control Functions In Models With Continuous Endogenous Treatment And Heterogeneous Effects written by Jean-Pierre Florens and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
We use the control function approach to identify the average treatment effect and the effect of treatment on the treated in models with a continuous endogenous regressor whose impact is heterogeneous. We assume a stochastic polynomial restriction on the form of the heterogeneity but, unlike alternative nonparametric control function approaches, our approach does not require large support assumptions.
Identification Of Treatment Effects Using Control Functions In Models With Continuous Endogenous Treatment And Heterogeneous Effects
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Author : Jean-Pierre Florens
language : en
Publisher:
Release Date : 2008
Identification Of Treatment Effects Using Control Functions In Models With Continuous Endogenous Treatment And Heterogeneous Effects written by Jean-Pierre Florens and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
We use the control function approach to identify the average treatment effect and the effect of treatment on the treated in models with a continuous endogenous regressor whose impact is heterogeneous. We assume a stochastic polynomial restriction on the form of the heterogeneity but, unlike alternative nonparametric control function approaches, our approach does not require large support assumptions.
A Unified Framework For Dynamic Treatment Effect Estimation In Interactive Fixed Effect Models
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Author : Nicholas Brown
language : en
Publisher:
Release Date : 2022
A Unified Framework For Dynamic Treatment Effect Estimation In Interactive Fixed Effect Models written by Nicholas Brown 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.
We present a unifying identification strategy of dynamic average treatment effect parameters for staggered interventions when parallel trends are valid only after controlling for interactive fixed effects. This setting nests the usual parallel trends assumption, but allows treated units to have heterogeneous exposure to unobservable macroeconomic trends. We show that any estimator that is consistent for the unobservable trends up to a non-singular rotation can be used to consistently estimate heterogeneous dynamic treatment effects. This result can apply to data sets with either many or few pre-treatment time periods. We also demonstrate the robustness of two-way fixed effects imputation to certain parallel trends violations and provide a test for its consistency. A quasi-long-differencing estimator is proposed and implemented to estimate the effect of Walmart openings on local economic conditions.
Nonparametric Bounds On Treatment Effects With Imperfect Instruments
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Author : Kyunghoon Ban
language : en
Publisher:
Release Date : 2020
Nonparametric Bounds On Treatment Effects With Imperfect Instruments written by Kyunghoon Ban and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Testing Exogeneity
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Author : Neil R. Ericsson
language : en
Publisher:
Release Date : 1994
Testing Exogeneity written by Neil R. Ericsson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Business & Economics categories.
This book discusses the nature of exogeneity, a central concept in standard econometrics texts, and shows how to test for it through numerous substantive empirical examples from around the world, including the UK, Argentina, Denmark, Finland, and Norway. Part I defines terms and provides the necessary background; Part II contains applications to models of expenditure, money demand, inflation, wages and prices, and exchange rates; and Part III extends various tests of constancy and forecast accuracy, which are central to testing super exogeneity. About the Series Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.
Identification And Estimation Of Local Average Treatment Effects
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Author : Guido W. Imbens
language : en
Publisher:
Release Date : 1995
Identification And Estimation Of Local Average Treatment Effects written by Guido W. Imbens 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.