On Jackknife-After-Bootstrap Method for Dependent Data

Beyaztas U., BEYAZTAŞ B. H.

COMPUTATIONAL ECONOMICS, cilt.53, sa.4, ss.1613-1632, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 53 Sayı: 4
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s10614-018-9827-4
  • Dergi Adı: COMPUTATIONAL ECONOMICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.1613-1632
  • Marmara Üniversitesi Adresli: Hayır

Özet

In this paper, we adapt sufficient and ordered non-overlapping block bootsrap methods into jackknife-after-bootstrap (JaB) algorithm to estimate the standard error of a statistic where observations form a stationary sequence. We also extend the JaB algorithm to obtain prediction intervals for future returns and volatilities of GARCH processes. The finite sample properties of the proposed methods are illustrated by an extensive simulation study and they are applied to S&P 500 stock index data. Our findings reveal that the proposed algorithm often exhibits improved performance and, is computationally more efficient compared to conventional JaB method.