2026-09-18T21:42:07Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/324082022-01-13T07:54:28Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Joshua D. Angrist, Victor Chernozhukov and Whitney K. Newey.
Fernández-Val, Iván
Massachusetts Institute of Technology. Dept. of Economics.
Massachusetts Institute of Technology. Department of Economics
2006-03-29T18:42:00Z
2006-03-29T18:42:00Z
2005
2005
http://hdl.handle.net/1721.1/32408
61695841
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2005.
Includes bibliographical references.
This dissertation is a collection of three independent essays in theoretical and applied econometrics, organized in the form of three chapters. In the first two chapters, I investigate the properties of parametric and semiparametric fixed effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in economics to analyze decisions such as labor force participation, union membership, migration, purchase of durable goods, marital status, or fertility. The second chapter looks at generalized method of moments estimation in panel data models with individual-specific parameters. An important example of these models is a random coefficients linear model with endogenous regressors. The third chapter (co-authored with Joshua Angrist and Victor Chernozhukov) studies the interpretation of quantile regression estimators when the linear model for the underlying conditional quantile function is possibly misspecified.
by Iván Fernández-Val.
Ph.D.
203 p.
9760338 bytes
9772654 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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
Economics.
Three essays on nonlinear panel data models and quantile regression analysis
3 essays on nonlinear panel data models and quantile regression analysis
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>This dissertation is a collection of three independent essays in theoretical and applied econometrics, organized in the form of three chapters. In the first two chapters, I investigate the properties of parametric and semiparametric fixed effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in economics to analyze decisions such as labor force participation, union membership, migration, purchase of durable goods, marital status, or fertility. The second chapter looks at generalized method of moments estimation in panel data models with individual-specific parameters. An important example of these models is a random coefficients linear model with endogenous regressors. The third chapter (co-authored with Joshua Angrist and Victor Chernozhukov) studies the interpretation of quantile regression estimators when the linear model for the underlying conditional quantile function is possibly misspecified.</Abstract>
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