Conditional variance estimation in heteroscedastic regression models

Lu-Hung Chen, Ming-Yen Cheng, Liang Peng

Research output: Contribution to journalArticlepeer-review

Abstract

First, we propose a new method for estimating the conditional variance in heteroscedasticity regression models. For heavy tailed innovations, this method is in general more efficient than either of the local linear and local likelihood estimators. Secondly, we apply a variance reduction technique to improve the inference for the conditional variance. The proposed methods are investigated through their asymptotic distributions and numerical performances.

Original languageEnglish
Pages (from-to)236-245
JournalJournal of Statistical Planning and Inference
Volume139
Issue number2
DOIs
Publication statusPublished - 1 Feb 2009

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