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 OJS  Vol.4 No.8 , September 2014
Modified Maximum Likelihood Estimation in Autoregressive Processes with Generalized Exponential Innovations
Bernardo Lagos-Álvarez1, Guillermo Ferreira1, Emilio Porcu2
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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.
Keywords: Autoregressive Time Series Model, Maximum Likelihood, Modified Maximum Likelihood, Least Squares, Generalized Exponential
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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