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Wong, W.K. and Bian, G. (2005) Estimating Parameters in Autoregressive Models with Asymmetric Innovations. Statistics and Probability Letters , 71, 61-70.
http://dx.doi.org/10.1016/j.spl.2004.10.022

has been cited by the following article:

• TITLE: Modified Maximum Likelihood Estimation in Autoregressive Processes with Generalized Exponential Innovations

  AUTHORS: Bernardo Lagos-Álvarez, Guillermo Ferreira, Emilio Porcu

  KEYWORDS: Autoregressive Time Series Model, Maximum Likelihood, Modified Maximum Likelihood, Least Squares, Generalized Exponential

  JOURNAL NAME: Open Journal of Statistics, Vol.4 No.8, September 23, 2014

  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.