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dc.contributor.advisorHonore, Bo Een_US
dc.contributor.advisorRedding, Stephen Jen_US
dc.contributor.authorCharbonneau, Karyne B.en_US
dc.contributor.otherEconomics Departmenten_US
dc.date.accessioned2013-09-16T17:27:29Z-
dc.date.available2013-09-16T17:27:29Z-
dc.date.issued2013en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp015q47rn86j-
dc.description.abstractThis dissertation studies nonlinear panel data models with multiple fixed effects. The first two chapters consider the adaptability of estimation methods for nonlinear panel data models to multiple fixed effects. The first chapter investigates whether existing methods can be modified to eliminate multiple fixed effects in the logit model and for Manski's (1987) maximum score estimator. I find that it is possible to generalize the conditional maximum likelihood approach of Rasch (1960,1961) to include two fixed effects for the former, but not for the latter. Monte Carlo simulations show that the conditional logit estimator presented in the chapter is as precise and less biased than other logit estimators. An application to trade data further highlights the importance of properly accounting for two fixed effects. The second chapter considers other nonlinear models. I find that it is possible to generalize the conditional maximum likelihood approach to include two fixed effects for the Poisson, Negative Binomial and Gamma regression models. I also find a moment condition allowing to consistently estimate the parameters for a multiplicative model with two fixed effects. Monte Carlo simulations show that both the Poisson estimating the fixed effects and the conditional Poisson estimator presented in the chapter perform well, while the conditional Negative Binomial estimator is less biased than its counterpart estimating the fixed effects. An application to trade data using the conditional Poisson estimator produces estimates of the gravity model parameters that differ significantly from those obtained with traditional estimators. The third chapter uses patent citations data to study the effect of geographical and technological distance on the diffusion of knowledge between regions of the U.S. I find strong evidence of localization, both geographically and technologically. The elasticity of distance is remarkably stable over time and is substantially higher for the first 18 months following the cited patent grant date. The effect of geographical and technological distance varies across technological categories, with ideas in the computers sector diffusing farther and faster. To deal with the count data nature of citations, I apply the multiple fixed effects Poisson and Negative Binomial estimators developed in the previous chapter.en_US
dc.language.isoenen_US
dc.publisherPrinceton, NJ : Princeton Universityen_US
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the <a href=http://catalog.princeton.edu> library's main catalog </a>en_US
dc.subjectFixed Effectsen_US
dc.subjectGravity Equationsen_US
dc.subjectKnowledge Diffusionen_US
dc.subjectLogiten_US
dc.subjectNegative Binomialen_US
dc.subjectPoissonen_US
dc.subject.classificationEconomicsen_US
dc.titleMultiple Fixed Effects in Theoretical and Applied Econometricsen_US
dc.typeAcademic dissertations (Ph.D.)en_US
pu.projectgrantnumber690-2143en_US
Appears in Collections:Economics

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