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General method of moments bias and specification tests for quantile regression

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dc.contributor.advisor Whitney Newey. en_US
dc.contributor.author Nejmeldeen, Ziad H. (Ziad Hassan), 1976- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Economics. en_US
dc.date.accessioned 2005-06-02T16:27:48Z
dc.date.available 2005-06-02T16:27:48Z
dc.date.copyright 2003 en_US
dc.date.issued 2003 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/17628
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2003. en_US
dc.description Includes bibliographical references (leaves 74-75). en_US
dc.description.abstract Chapter 1: This chapter looks at a dynamic panel data model with fixed effects. Estimating the model with GMM is consistent but suffers from small sample bias. We apply Helmert's transformation to the model, assume that error terms and nuisance parameters are homoskedastic and independent across observations and of one another, and utilize the GMM bias calculation of Newey & Smith (2001). This leads to a closed form expression for the GMM bias applied to AR(1) model. Chapter 2: This chapter develops specification tests for quantile regression under various data types. We consider what happens to the quantile regression estimator under local and global misspecification and design specification tests that handle a wide range of data types. We consider how to carry out such tests in practice and present Monte Carlo results to show the effectiveness of such tests. Chapter 3: Through a Taylor expansion, We compute the bias of a general GMM model where the weighting matrix A of the moment conditions g(z, β) is left unspecified, except for some general conditions. Our bias results are compared to those of Newey and West (2003). An important case of GMM estimation with a general weighting matrix A is when A is a function of a vector of parameters with fixed dimension. Arellano's IVE estimator is an example of this type of estimator--we consider the bias properties of Arellano's IVE estimator in the AR(1) setting and compare them to our results from Chapter 1. en_US
dc.description.statementofresponsibility by Ziad H. Nejmeldeen. en_US
dc.format.extent 78 leaves en_US
dc.format.extent 2134899 bytes
dc.format.extent 2134706 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Economics. en_US
dc.title General method of moments bias and specification tests for quantile regression en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Economics. en_US
dc.identifier.oclc 54771126 en_US


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