Modified Maximum Likelihood Estimation in Autoregressive Processes with Generalized Exponential Innovations

作     者：Bernardo Lagos-álvarez Guillermo Ferreira Emilio Porcu 

作者机构：Department of Statistics Universidad de Concepción Concepción Chile Department of Mathematics University Federico Santa María Valparaíso Chile 

出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)

年 卷 期：2014年第4卷第8期

页      面：620-629页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Autoregressive Time Series Model Maximum Likelihood Modified Maximum Likelihood Least Squares Generalized Exponential 

摘      要：We consider a time series following a simple linear regression with first-order autoregressive errors belonging to the class of heavy-tailed distributions. The proposed model provides a useful generalization of the symmetrical linear regression models with independent error, since the error distribution covers both correlated innovations following a Generalized Exponential distribution. Furthermore, we derive the modified maximum likelihood (MML) estimators as an efficient alternative for estimating model parameters. Finally, we investigate the asymptotic properties of the proposed estimators. Our findings are also illustrated through a simulation study.