dc.contributor.advisor | Krivobokova, Tatyana Prof. Dr. | de |
dc.contributor.author | Wiesenfarth, Manuel | de |
dc.date.accessioned | 2012-07-04T16:03:16Z | de |
dc.date.accessioned | 2013-01-18T13:53:52Z | de |
dc.date.available | 2013-01-30T23:50:56Z | de |
dc.date.issued | 2012-07-04 | de |
dc.identifier.uri | http://hdl.handle.net/11858/00-1735-0000-000D-F09E-B | de |
dc.identifier.uri | http://dx.doi.org/10.53846/goediss-3021 | |
dc.description.abstract | Semiparametrische additive
Regressionsmodelle schw | de |
dc.format.mimetype | application/pdf | de |
dc.language.iso | eng | de |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | de |
dc.title | Estimation and Inference in Special Nonparametric Models with Applications to Topics in Development Economics | de |
dc.type | doctoralThesis | de |
dc.title.translated | Schätzung und Inferenz in speziellen nichtparametrischen Modellen mit Andwendungen in der Entwicklungsökonomie | de |
dc.contributor.referee | Krivobokova, Tatyana Prof. Dr. | de |
dc.date.examination | 2012-05-11 | de |
dc.subject.dnb | 310 Statistik | de |
dc.subject.gok | EGCG 080 | de |
dc.description.abstracteng | Semiparametric additive regression models
relax the common assumption of covariate effects to have a known
functional form specified by some polynomial. These models only
assume that relationships are smooth and are additively separable
and thus are less restrictive than common parametric approaches.
Estimation techniques for such models assuming all covariates to be
exogenous, i.e. uncorrelated to the error term ruling out the
presence of confounding omitted variables, for example, have become
widely available. However, additive models with weaker assumptions
on the error term and methods for inference such as simultaneous
confidence bands and specification tests are still subject to
extensive research. Thus, the objectives of the thesis are the
development of flexible methods for estimation and inference and
their application in various complex data situations. Thereby,
particular focus is laid on the computational implementation of all
proposed approaches aiming at the provision of user-friendly
software packages.
First, the determinants of chronic child undernutrition in Kenya
are analyzed. Particular research questions include the possibility
of catch-up growth, i.e. improvements of the nutritional status
over age, and relevance of hypotheses on the functional forms of
certain effects. In order to address these questions, simultaneous
confidence bands for additive models with locally-adaptive smoothed
components and heteroscedastic errors are proposed. These
appropriately quantify the estimation uncertainty of function
estimates and can be used for assessing the statistical
significance of an effect and of certain features in a curve.
Further, a powerful nonparametric specification test is introduced
that allows to test for polynomial regression versus nonparametric
alternatives.
Next, the needs-relatedness of relief supply in earthquake-affected
communities in Pakistan is studied. Here, non-random sample
selection calls for the application of a sample selection model
with flexible spatial and time-varying effects accounting for
unobserved regional heterogeneity and for varying survivor needs
over changing seasonal conditions. A flexible Bayesian approach to
correct for the sample selection bias is proposed that allows to
simultaneously estimate the determinants of the probability to
receive relief supply and of the amount of delivered supply.
Finally, the usual assumption of the unobservable error term to be
orthogonal to the covariates is relaxed relying on the availability
of some instrumental variable. A Bayesian nonparametric
instrumental variable approach is proposed where bias correction
relies on a simultaneous equations specification with flexible
modeling of both the covariate effects and the joint error
distribution. The approach is used to analyze the relationship
between class size and scholastic achievements of students in
Israel. | de |
dc.contributor.coReferee | Klasen, Stephan Prof. Dr. | de |
dc.contributor.thirdReferee | Kneib, Thomas Prof. Dr. | de |
dc.subject.topic | Economics | de |
dc.subject.ger | adaptive Glättung | de |
dc.subject.ger | additive Modelle | de |
dc.subject.ger | bayesianische P-splines | de |
dc.subject.ger | Instrumentalvariablen | de |
dc.subject.ger | Penalized Splines | de |
dc.subject.ger | Simultane Konfidenzbänder | de |
dc.subject.ger | Spezifikationstest | de |
dc.subject.ger | Unterernährung | de |
dc.subject.eng | adaptive smoothing | de |
dc.subject.eng | additive model | de |
dc.subject.eng | Bayesian P-splines | de |
dc.subject.eng | instrumental variables | de |
dc.subject.eng | penalized splines | de |
dc.subject.eng | sample selection model | de |
dc.subject.eng | simultaneous confidence bands | de |
dc.subject.eng | specification test | de |
dc.subject.eng | undernutrition | de |
dc.subject.bk | 31.73 | de |
dc.subject.bk | 83.03 | de |
dc.subject.bk | 83.46 | de |
dc.identifier.urn | urn:nbn:de:gbv:7-webdoc-3599-4 | de |
dc.identifier.purl | webdoc-3599 | de |
dc.affiliation.institute | Wirtschaftswissenschaftliche Fakultät | de |
dc.identifier.ppn | 737899212 | de |