Likelihood-Based Inference in Autoregressive Models with Scaled t-Distributed Innovations by Means of EM-Based Algorithms

作     者：Haghbin, H. Nematollahi, A. R. 

作者机构：Shiraz Univ Dept Stat Shiraz Iran 

出 版 物：《COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION》 (Commun. Stat. Simul. Comput.)

年 卷 期：2013年第42卷第10期

页      面：2239-2252页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：Autoregressive process ECM algorithm ECME algorithm EM algorithm t-distribution 

摘      要：This article applies the EM-based (ECM and ECME) algorithms to find the maximum likelihood estimates of model parameters in general AR models with independent scaled t-distributed innovations whenever the degrees of freedom are unknown. The ECME, sharing advantages with both EM and NewtonRaphson algorithms, is an extension of ECM, which itself is an extension of the EM algorithm. The ECM and ECME algorithms, which are analytically quite simple to use, are then compared based on the computational running time and the accuracy of estimation via a simulation study. The results demonstrate that the ECME is efficient and usable in practice. We also show how our method can be applied to the Wolfer s sunspot data.