Title page for ETD etd-0698-184217


Type of Document Dissertation
Author Paczkowski, Remi
Author's Email Address remigius@vt.edu
URN etd-0698-184217
Title Monte Carlo Examination of Static and Dynamic Student t Regression Models
Degree PhD
Department Agricultural and Applied Economics
Advisory Committee
Advisor Name Title
McGuirk, Anya M. Committee Chair
Anderson-Cook, Christine M. Committee Member
Driscoll, Paul J. Committee Member
Hoepner, Paul H. Committee Member
Taylor, Daniel B. Committee Member
Keywords
  • Static Student t Regression Model
  • Dynamic Student t Regression Model
  • Student t Autoregressive Model
  • Monte Carlo experiment
  • Maximum Likelihood estimation
Date of Defense 1997-09-01
Availability restricted
Abstract
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.

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