|Title:||Monte Carlo Examination of Static and Dynamic Student t Regression Models|
|Degree:||Doctor of Philosophy|
|Department:||Agricultural and Applied Economics|
|Committee Chair:||Anya McGuirk|
|Committee Members:||Paul Hoepner|
|Keywords:||Static Student t Regression Model, Dynamic Student t Regression Model, Student t Autoregressive Model, Monte Carlo experiment, Maximum Likelihood estimation|
|Date of defense:||September 1, 1997|
|Availability:||Release the entire work for Virginia Tech access only.
After one year release worldwide only with written permission of the student and the advisory committee chair.
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 modelsí 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.
List of Attached Files
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