Empirical economic studies are often confronted by the joint problem of weak instruments and near exogeneity, such as labor economics and empirical economic growth theory. This dissertation presents new evidence and solutions on estimation and inference with weak instruments and near exogeneity.
Chapter 1 reexamines the effect of institutions on economic performance in Acemoglu, Johnson and Robinson (2001) where the measurement of current institutions is instrumented by European settler mortality rates. Since many economists argue that the settler mortality rates can possibly affect economic performance through other channels, I reexamine the effect of institutions by considering near exogeneity. I provide some evidence to show that the effect of institutions is not significant in many regression specifications when the settler mortality rates are used as the main instrument.
Chapter 2 studies estimation and inference with weak instruments and near exogeneity in a linear simultaneous equations model. I show that near exogeneity can exaggerate asymptotic bias of the TSLS and the LIML estimators. When using critical values from chi-square distributions, Anderson-Rubin and Kleibergen tests under exogeneity have a large size distortion. I propose the delete-d jackknife based Anderson-Rubin and Kleibergen tests to automatically reduce the size distortion in finite samples without a need for any pretest of exogeneity.
Chapter 3 extends estimation and inference with weak identification and near exogeneity into a GMM framework with instrumental variables. A GMM framework allows nonlinear and nondifferentiable moment conditions. I examine asymptotic results of one-step GMM estimator, two-step efficient GMM estimator and continuously updating estimator with weak identification and near exogeneity. Near exogeneity can produce relatively large bias for all these estimators. The Anderson-Rubin type and the Kleibergen type tests under near exogeneity converge in distribution to nonstandard distributions, which creates large size distortion when using critical values from chi-square distributions. The delete-d jackknife based approach can reduce the size distortion
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-04212006-171942 |
| Date | 06 July 2006 |
| Creators | Fang, Ying |
| Contributors | Jean-Francois Richard, Daniel Berkowitz, Karen Clay, Mehmet Caner, Nese Yildiz |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-04212006-171942/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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