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Pseudo‐likelihood estimation in ARCH models
Author(s) -
Mukherjee Kanchan
Publication year - 2006
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550340210
Subject(s) - estimator , heteroscedasticity , mathematics , maximum likelihood , moment (physics) , autoregressive model , class (philosophy) , econometrics , statistics , estimation , method of moments (probability theory) , computer science , economics , artificial intelligence , physics , management , classical mechanics
The author presents asymptotic results for the class of pseudo‐likelihood estimators in the autoregressive conditional heteroscedastic models introduced by Engle (1982). Unlike what is required for the quasi‐likelihood estimator, some estimators in the class he considers do not require the finiteness of the fourth moment of the error density. Thus his method is applicable to heavy‐tailed error distributions for which moments higher than two may not exist.