Gayle, Wayne-Roy
(2007)
Contributions to Structural Modeling and Estimation.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
<p>The first chapter of my thesis develops and estimates a dynamic<br />structural partial equilibrium model of schooling and work<br />decisions. The estimated model explicitly accounts for the<br />simultaneous choice of enrolling in school and working. It also<br />allows for endogenous leisure choices, intertemporal<br />nonseparabilities in preferences, aggregate skill specific<br />productivity shocks, aggregate consumption price effects, and<br />individual heterogeneity. Times spent on schooling, working, and<br />leisure are treated as continuous choice variables. The estimated<br />model is solved and two counterfactual simulation exercises are<br />performed. The first is the case where a subsidy is given to<br />individuals who enroll in school and do not participate in the labor<br />market. The second is the case where the demands of the school<br />curriculum are increased so that a young man enrolled in school<br />necessarily spends more time studying. The conclusion is that the<br />latter policy is more effective in enhancing educational<br />achievements and future wages.</p><p>The second chapter of my thesis develops a semiparametric estimator<br />for a dynamic nonlinear single index panel data model. Flexibility<br />of the model is achieved by assuming that the index function is<br />unknown. Flexibility in individual heterogeneity is achieved by<br />assuming that the individual effect is an unknown function of some<br />observable random variable. The paper proposes an algorithm that<br />estimates each of the finite and infinite dimensional parameters. In<br />particular, the full data generating process is estimated. This is<br />important if the predicted outcomes are used as plug-in estimators,<br />as in the multistage estimation of dynamic structural models.</p><p>The final chapter of my thesis develops a powerful new algorithm to<br />solve single object first price auctions where bidders draw<br />independent private values from heterogeneous distributions. The<br />algorithm allows for the scenario in which groups of symmetric and<br />asymmetric bidders may collude, and for the auctioneer to set a<br />reserve price. The paper also provides operational univariate<br />quadratures to evaluate the probabilities of winning as well as the<br />expected revenues for the bidders and the auctioneer. The expected<br />revenue function is used to the compute optimal reserve under<br />asymmetric environments.</p>
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
26 September 2007 |
| Date Type: |
Completion |
| Defense Date: |
27 November 2006 |
| Approval Date: |
26 September 2007 |
| Submission Date: |
7 March 2007 |
| Access Restriction: |
5 year -- Restrict access to University of Pittsburgh for a period of 5 years. |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Economics |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Asymmetric Auctions; Educational Attainment; Individual Heterogeneity; Labor Supply; Numerical Solutions; Panel Data; Seimiparametric |
| Other ID: |
http://etd.library.pitt.edu/ETD/available/etd-03072007-104200/, etd-03072007-104200 |
| Date Deposited: |
10 Nov 2011 19:32 |
| Last Modified: |
15 Nov 2016 13:36 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/6446 |
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