Monte Carlo Examination of Static and Dynamic Student t Regression Models

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dc.contributor.authorPaczkowski, Remien
dc.contributor.committeechairMcGuirk, Anya M.en
dc.contributor.committeememberDriscoll, Paul J.en
dc.contributor.committeememberTaylor, Daniel B.en
dc.contributor.committeememberAnderson-Cook, Christine M.en
dc.contributor.committeememberHoepner, Paul H.en
dc.contributor.departmentAgricultural and Applied Economicsen
dc.date.accessioned2014-03-14T21:15:16Zen
dc.date.adate1998-01-07en
dc.date.available2014-03-14T21:15:16Zen
dc.date.issued1997-09-01en
dc.date.rdate1999-01-07en
dc.date.sdate1997-09-01en
dc.description.abstractThis dissertation examines a number of issues related to Static and Dynamic Student t Regression Models. The Static Student t Regression Model is derived and transformed to an operational form. The operational form is then examined in a series of Monte Carlo experiments. The model is judged based on its usefulness for estimation and testing and its ability to model the heteroskedastic conditional variance. It is also compared with the traditional Normal Linear Regression Model. Subsequently the analysis is broadened to a dynamic setup. The Student t Autoregressive Model is derived and a number of its operational forms are considered. Three forms are selected for a detailed examination in a series of Monte Carlo experiments. The models’ usefulness for estimation and testing is evaluated, as well as their ability to model the conditional variance. The models are also compared with the traditional Dynamic Linear Regression Model.en
dc.description.degreePh. D.en
dc.identifier.otheretd-0698-184217en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-0698-184217/en
dc.identifier.urihttp://hdl.handle.net/10919/38691en
dc.publisherVirginia Techen
dc.relation.haspartETD.PDFen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectStatic Student t Regression Modelen
dc.subjectDynamic Student t Regression Modelen
dc.subjectStudent t Autoregressive Modelen
dc.subjectMonte Carlo experimenten
dc.subjectMaximum Likelihood estimationen
dc.titleMonte Carlo Examination of Static and Dynamic Student t Regression Modelsen
dc.typeDissertationen
thesis.degree.disciplineAgricultural and Applied Economicsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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