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University of Illinois at Urbana-Champaign
Estimation and inference for conditionally heteroscedastic models
Abstract
dc:descriptionThe 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.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Statistics
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2011
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Zhao, Quanshui
- Contributors dc:contributor
-
- Portnoy, Stephen L.
Subjects
dc:subject × 1Rights
dc:rights- Statement dc:rights
-
- Copyright 1995 Zhao, Quanshui
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
-
AAI9624549
(UMI)AAI9624549 - OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/21193