Estimating parameters in autoregressive models in non-normal situations: Asymmetric innovations

Date

2001-01-01

Author

Akkaya, Ayşen

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The estimation of coefficients in a simple autoregressive model is considered in a supposedly difficult situation where the innovations have an asymmetric distribution. Two distributions, gamma and generalized logistic, are considered for illustration. Closed form estimators are obtained and shown to be efficient and robust. Efficiencies of least squares estimators are evaluated and shown to be very low. This work is an extension of that of Tiku, Wong and Bian [1] who give solutions for a simple AR(I) model

Subject Keywords

Autoregression, Skewness, Maximum likelihood, Modified maximum likelihood, Least squares, Robustness, Chisquare, Generalized logistic, Autocorrelation

URI

https://hdl.handle.net/11511/52274

Journal

Communications in Statistics - Theory and Methods

DOI

https://doi.org/10.1081/sta-100002095

Collections

Department of Statistics, Article

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Citation Formats

A. Akkaya, “Estimating parameters in autoregressive models in non-normal situations: Asymmetric innovations,” Communications in Statistics - Theory and Methods, pp. 517–536, 2001, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52274.