Maximum likelihood estimators in regression models with infinite variance innovations

被引：6
|
作者：
Paulaauskas, V
Rachev, ST
机构：
[1] Vilnius State Univ, Dept Math, Vilnius, Lithuania
[2] Univ Karlsruhe, Inst Stat & Math Econ, Karlsruhe, Germany
[3] Univ Calif Santa Barbara, Dept Stat & Appl Probabil, Santa Barbara, CA 93106 USA
来源：
STATISTICAL PAPERS | 2003年 / 44卷 / 01期
关键词：
autoregression; stable distributions; Levy processes; maximum likelihood estimators;
D O I：
10.1007/s00362-002-0133-8
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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.
引用
收藏
页码：47 / 65
页数：19
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