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

Akkaya A., Tiku M.

Communications in Statistics - Theory and Methods, vol.30, no.3, pp.517-536, 2001 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 3
  • Publication Date: 2001
  • Doi Number: 10.1081/sta-100002095
  • Journal Name: Communications in Statistics - Theory and Methods
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.517-536
  • Keywords: autoregression, skewness, maximum likelihood, modified maximum likelihood, least squares, robustness, chisquare, generalized logistic, autocorrelation, ORDER-STATISTICS, COVARIANCES, REGRESSION, VARIANCES, LOCATION
  • Middle East Technical University Affiliated: Yes

Abstract

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.