TY - JOUR
T1 - Conditional variance estimation in heteroscedastic regression models
AU - Chen, Lu-Hung
AU - Cheng, Ming-Yen
AU - Peng, Liang
N1 - Cheng's research was supported in part by NSC Grant NSC-94-2118-M-002-002 and Mathematics Division, National Center for Theoretical Sciences (Taipei Office). Peng's research was supported by NSF Grants DMS-04-03443, SES-0631608, the Humboldt research fellowship and HKSAR-RGC Grant no. 400306.
PY - 2009/2/1
Y1 - 2009/2/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-55149112598&partnerID=MN8TOARS
U2 - 10.1016/j.jspi.2008.04.020
DO - 10.1016/j.jspi.2008.04.020
M3 - Journal article
SN - 0378-3758
VL - 139
SP - 236
EP - 245
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
IS - 2
ER -