TY - JOUR
T1 - Maximum likelihood estimators in regression models with infinite variance innovations
AU - Paulaauskas, Vygantas
AU - Rachev, Svetlozar T.
PY - 2003/1
Y1 - 2003/1
N2 - In this paper we consider the problem of maximum likelihood (ML) estimation in the classical AR(1) model with i.i.d. symmetric stable innovations with known characteristic exponent and unknown scale parameter. We present an approach that allows us to investigate the properties of ML estimators without making use of numerical procedures. Finally, we introduce a generalization to the multivariate case.
AB - In this paper we consider the problem of maximum likelihood (ML) estimation in the classical AR(1) model with i.i.d. symmetric stable innovations with known characteristic exponent and unknown scale parameter. We present an approach that allows us to investigate the properties of ML estimators without making use of numerical procedures. Finally, we introduce a generalization to the multivariate case.
KW - Autoregression
KW - Lévy processes
KW - Maximum likelihood estimators
KW - Stable distributions
UR - http://www.scopus.com/inward/record.url?scp=0038576993&partnerID=8YFLogxK
U2 - 10.1007/s00362-002-0133-8
DO - 10.1007/s00362-002-0133-8
M3 - Article
AN - SCOPUS:0038576993
VL - 44
SP - 47
EP - 65
JO - Statistical Papers
JF - Statistical Papers
SN - 0932-5026
IS - 1
ER -