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Semiparametric instrumental variable methods for causal response models

Author(s)
Abadie, Alberto, 1968-
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Massachusetts Institute of Technology. Dept. of Economics.
Advisor
Joshua D. Angrist and Whitney K. Newey.
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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
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Abstract
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.
Description
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Economics, c1999.
 
Includes bibliographical references.
 
Date issued
1999
URI
http://hdl.handle.net/1721.1/38857
Department
Massachusetts Institute of Technology. Department of Economics
Publisher
Massachusetts Institute of Technology
Keywords
Economics.

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