Semiparametric instrumental variable methods for causal response models
Name
43838892-MIT.pdf
Description
Full printable version
Size
5.01 MB
Format
Adobe PDF
Checksum (MD5)
49f464c9d61dc65946d05233ee8f89de
Author(s)
Abadie, Alberto, 1968-
Advisor(s)
Joshua D. Angrist and Whitney K. Newey.
Date Issued
1999
Publisher
Massachusetts Institute of Technology
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.
Subjects
Economics.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
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