Semiparametric instrumental variable methods for causal response models

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Economics, c1999.

Bibliographic Details
Main Author: Abadie, Alberto, 1968-
Other Authors: Joshua D. Angrist and Whitney K. Newey.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2007
Subjects:
Online Access:http://hdl.handle.net/1721.1/38857
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author Abadie, Alberto, 1968-
author2 Joshua D. Angrist and Whitney K. Newey.
author_facet Joshua D. Angrist and Whitney K. Newey.
Abadie, Alberto, 1968-
author_sort Abadie, Alberto, 1968-
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description Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Economics, c1999.
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spelling mit-1721.1/388572019-09-26T22:41:20Z Semiparametric instrumental variable methods for causal response models Abadie, Alberto, 1968- Joshua D. Angrist and Whitney K. Newey. Massachusetts Institute of Technology. Dept. of Economics. Massachusetts Institute of Technology. Department of Economics Economics. Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Economics, c1999. Includes bibliographical references. This dissertation proposes new instrumental variable methods to identify, estimate and test for causal effects of endogenous treatments. These new methods are distinguished by the combination of nonparametric identifying assumptions and semiparametric estimators that provide a parsimoniuous summary of the results. The thesis consists of three essays presented in the form of chapters. The first chapter shows how to estimate linear and nonlinear causal response functions with covariates under weak (instrumental variable) identification restrictions. The second chapter (co-authored with Joshua Angrist and Guido Imbens) applies the identification results of the first chapter to estimate quantile causal response functions, so we can study the effect of the treatment on different parts of the distribution of the outcome variable. The third chapter of this dissertation looks again at distributional effects but focusing directly on the cumulative distribution functions of the potential outcomes with and without the treatment. by Alberto Abadie. Ph.D. 2007-08-29T21:02:26Z 2007-08-29T21:02:26Z 1999 1999 Thesis http://hdl.handle.net/1721.1/38857 43838892 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 96 leaves application/pdf Massachusetts Institute of Technology
spellingShingle Economics.
Abadie, Alberto, 1968-
Semiparametric instrumental variable methods for causal response models
title Semiparametric instrumental variable methods for causal response models
title_full Semiparametric instrumental variable methods for causal response models
title_fullStr Semiparametric instrumental variable methods for causal response models
title_full_unstemmed Semiparametric instrumental variable methods for causal response models
title_short Semiparametric instrumental variable methods for causal response models
title_sort semiparametric instrumental variable methods for causal response models
topic Economics.
url http://hdl.handle.net/1721.1/38857
work_keys_str_mv AT abadiealberto1968 semiparametricinstrumentalvariablemethodsforcausalresponsemodels