Time series AR(1) model for short-tailed distributions

Akkaya A., Tiku M.

STATISTICS, cilt.39, sa.2, ss.117-132, 2005 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 2
  • Basım Tarihi: 2005
  • Doi Numarası: 10.1080/02331880512331344036
  • Dergi Adı: STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.117-132
  • Anahtar Kelimeler: time series, non-normality, short-tailedness, inliers, skewness, modified likelihood, robustness, hypothesis testing
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

The innovations in AR(1) models in time series have primarily been assumed to have a normal or long-tailed distributions. We consider short-tailed distributions (kurtosis less than 3) and derive modified maximum likelihood (MML) estimators. We show that the MML estimator of 0 is considerably more efficient than the commonly used least squares estimator and is also robust. This paper is essentially the first to achieve robustness to inliers and to various forms of short-tailedness in time series analysis.