Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models

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Beyaztas U., Shang H. L.

JOURNAL OF APPLIED STATISTICS, cilt.49, sa.5, ss.1179-1202, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 49 Sayı: 5
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1080/02664763.2020.1856351
  • Dergi Adı: JOURNAL OF APPLIED STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Veterinary Science Database, zbMATH
  • Sayfa Sayıları: ss.1179-1202
  • Anahtar Kelimeler: Autoregression, multivariate forecast, prediction interval, resampling methods, vector autoregression, weighted likelihood
  • Marmara Üniversitesi Adresli: Evet

Özet

The bootstrap procedure has emerged as a general framework to construct prediction intervals for future observations in autoregressive time series models. Such models with outlying data points are standard in real data applications, especially in the field of econometrics. These outlying data points tend to produce high forecast errors, which reduce the forecasting performances of the existing bootstrap prediction intervals calculated based on non-robust estimators. In the univariate and multivariate autoregressive time series, we propose a robust bootstrap algorithm for constructing prediction intervals and forecast regions. The proposed procedure is based on the weighted likelihood estimates and weighted residuals. Its finite sample properties are examined via a series of Monte Carlo studies and two empirical data examples.