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	Doctor of Philosophy
Department	Agricultural and Applied Economics
Advisory Committee	Advisor Name Title Anya McGuirk Committee Chair Christine Anderson-Cook Committee Member Daniel Taylor Committee Member Paul Driscoll Committee Member Paul Hoepner 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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