Estimation and inference for conditionally heteroscedastic models
Zhao, Quanshui
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https://hdl.handle.net/2142/21193
Description
Title
Estimation and inference for conditionally heteroscedastic models
Author(s)
Zhao, Quanshui
Issue Date
1995
Doctoral Committee Chair(s)
Portnoy, Stephen L.
Department of Study
Statistics
Discipline
Statistics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Statistics
Language
eng
Abstract
The ordinary least squares (OLS) method is known to be efficient for linear models when the errors are homogeneous with Gaussian distributions, but troublesome with heteroscedastic or non-Gaussian errors. For the latter nonstandard case, we use the weighted quantile regression (l$\sb1$) method, gaining both robustness and efficiency, with successful applications to interval forecasting of ARCH type time series models.
Dynamically changing regression parameters are another discrepancy to the ordinary linear models. By using the recursive method, the dynamically evolving parameters can be estimated. Asymptotic properties are studied for paired comparison models (a chess rating system) and dynamic ARCH models.
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