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Modified Maximum Likelihood Estimation in Autoregressive Processes with Generalized Exponential Innovations

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DOI: 10.4236/ojs.2014.48058    3,269 Downloads   4,003 Views  

ABSTRACT

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Lagos-Álvarez, B. , Ferreira, G. and Porcu, E. (2014) Modified Maximum Likelihood Estimation in Autoregressive Processes with Generalized Exponential Innovations. Open Journal of Statistics, 4, 620-629. doi: 10.4236/ojs.2014.48058.

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