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

Akkaya A., Tiku M.

Communications in Statistics - Theory and Methods, cilt.30, sa.3, ss.517-536, 2001 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 30 Sayı: 3
  • Basım Tarihi: 2001
  • Doi Numarası: 10.1081/sta-100002095
  • Dergi Adı: Communications in Statistics - Theory and Methods
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.517-536
  • Anahtar Kelimeler: autoregression, skewness, maximum likelihood, modified maximum likelihood, least squares, robustness, chisquare, generalized logistic, autocorrelation, ORDER-STATISTICS, COVARIANCES, REGRESSION, VARIANCES, LOCATION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.