Monte Carlo Examination of Static and Dynamic Student t Regression Models

This 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.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Agricultural and Applied Economics

Department dc:contributor.department

Agricultural and Applied Economics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

1997

Author dc:creator

• Paczkowski, Remi

Chair dc:contributor.committeechair

• McGuirk, Anya M.

Committee members dc:contributor.committeemember

• Driscoll, Paul J.
• Taylor, Daniel B.
• Anderson-Cook, Christine M.
• Hoepner, Paul H.

• Static Student t Regression Model
• Dynamic Student t Regression Model
• Student t Autoregressive Model
• Monte Carlo experiment
• Maximum Likelihood estimation

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-0698-184217

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/38691