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Activity Number:
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514
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2008 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #301226 |
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Title:
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On Robust Estimation of the Heteroscedasticity Covariance Matrix
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Author(s):
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Tsung-Chi Cheng*+
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Companies:
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National Cheng-Chi University
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Address:
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64 ZhihNan Road, Section 2, Taipei, 11605, Taiwan
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Keywords:
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Heteroscedasticity ; L1 estimator ; robust diagnostics
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Abstract:
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The assumption of homogenous variance in the normal regression model is not always appropriate. The invalidity of standard inference procedure may be produced due to the wrong estimation of the standard error when the disturbance process in a regression model presents heteroscedasticity. Test based on a heteroscedasticity consistent covariance matrix (short for HCCM) estimator is popular in application because there is no need to specify the structural form of heteroscedasticity and it is easy to compute (White 1985). The leverage points are decisive for the finite sample behavior than the degree of heteroscedasticity in the estimation of HCCM (Cribari-Neto and Zarkos 2001; and Cribari-Neto 2004). In this paper we propose a robust estimator for the heteroscedasticity covariance matrix, which is based on the concept of robust weighted L1 estimates.
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