Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.spl.2004.10.022
Title: Estimating parameters in autoregressive models with asymmetric innovations
Authors: Wong, W.-K. 
Bian, G.
Keywords: Autoregression
Generalized logistic distribution
Least squares
Modified maximum likelihood
Nonnormality
Robustness
Issue Date: 2005
Source: Wong, W.-K., Bian, G. (2005). Estimating parameters in autoregressive models with asymmetric innovations. Statistics and Probability Letters 71 (1) : 61-70. ScholarBank@NUS Repository. https://doi.org/10.1016/j.spl.2004.10.022
Abstract: Tiku et al. (Theory Methods 28(2) (1999) 315) considered the estimation in a regression model with autocorrelated error in which the underlying distribution be a shift-scaled Student's t distribution, developed the modified maximum likelihood (MML) estimators of the parameters and showed that the proposed estimators had closed forms and were remarkably efficient and robust. In this paper, we extend the results to the case, where the underlying distribution is a generalized logistic distribution. The generalized logistic distribution family represents very wide skew distributions ranging from highly right skewed to highly left skewed. Analogously, we develop the MML estimators since the ML (maximum likelihood) estimators are intractable for the generalized logistic data. We then study the asymptotic properties of the proposed estimators and conduct simulation to the study. © 2004 Elsevier B.V. All rights reserved.
Source Title: Statistics and Probability Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/22375
ISSN: 01677152
DOI: 10.1016/j.spl.2004.10.022
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