The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Bootstrap estimation of long-run variance under strong dependence

장기간 의존 시계열에서 붓스트랩을 이용한 장기적 분산 추정

  • Baek, Changryong (Department of Statistics, Sungkyunkwan University) ;
  • Kwon, Yong (Department of Statistics, Sungkyunkwan University)
  • 백창룡 (성균관대학교 통계학과) ;
  • 권용 (성균관대학교 통계학과)
  • Received : 2016.01.13
  • Accepted : 2016.02.29
  • Published : 2016.04.30
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Abstract

This paper considers a long-run variance estimation using a block bootstrap method under strong dependence also known as long range dependence. We extend currently available methods in two ways. First, it extends bootstrap methods under short range dependence to long range dependence. Second, to accommodate the observation that strong dependence may come from deterministic trend plus noise models, we propose to utilize residuals obtained from the nonparametric kernel estimation with the bimodal kernel. The simulation study shows that our method works well; in addition, a data illustration is presented for practitioners.

본 논문은 시계열 분석의 추론에서 매우 중요한 역할을 하는 장기적 분산에 대해서 붓스트랩을 이용한 추정을 다룬다. 본 논문은 기존의 방법을 두가지 측면에서 확장한다. 첫째, 단기억 시계열에서의 장기적 분산 추정을 확장하여 자료의 의존성이 매우 강한 장기간 의존 시계열에서 붓스트랩을 이용한 장기적 분산의 추정에 대해서 논의한다. 또한 장기간 의존 시계열이 평균변화모형과 매우 쉽게 잘 혼동됨이 잘 알려져 있기에 이를 해결하기 위해서 쌍봉형 커널을 이용한 추세 추정 및 붓스트랩의 블럭을 결정하는 방법을 제안한다. 모의 실험결과 제안한 방법이 매우 유의하였으며 북반구 평균 온도 변화 자료 분석으로 실증 자료 예제도 아울러 제시하였다.

Keywords

References

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