응용통계연구 (The Korean Journal of Applied Statistics)

  • 제28권3호
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  • Pages.583-592
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  • 2015
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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조건부 포아송 및 음이항 분포를 이용한 영-과잉 INGARCH 자료 분석

Zero-Inflated INGARCH Using Conditional Poisson and Negative Binomial: Data Application

  • 윤재은 (숙명여자대학교 통계학과) ;
  • 황선영 (숙명여자대학교 통계학과)
  • Yoon, J.E. (Department of Statistics, Sookmyung Women's University) ;
  • Hwang, S.Y. (Department of Statistics, Sookmyung Women's University)
  • 투고 : 2015.05.26
  • 심사 : 2015.06.09
  • 발행 : 2015.06.30
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초록

영-과잉(zero-inflation) 현상은 최근 계수(count) 시계열 분석의 주요토픽으로 다루어지고 있다. 본 논문에서는 영-과잉 계수 시계열의 변동성을 연구하고 있다. 기존의 정수형 모형인 INGARCH(integer valued GRACH) 모형에 조건부 포아송 및 조건부 음이항 분포를 사용하여 변동성에 영-과잉 현상을 추가하였다. 모수 추정 방법으로 EM알고리즘을 사용하였으며 국내 콜레라 발생건수에 적용시켜 보았다.

Zero-inflation has recently attracted much attention in integer-valued time series. This article deals with conditional variance (volatility) modeling for the zero-inflated count time series. We incorporate zero-inflation property into integer-valued GARCH (INGARCH) via conditional Poisson and negative binomial marginals. The Cholera frequency time series is analyzed as a data application. Estimation is carried out using EM-algorithm as suggested by Zhu (2012).

키워드

참고문헌

  1. Ferland, R., Latour, A. and Oraichi, D. (2006). Integer-valued GARCH process, Journal of Time Series Analysis, 27, 923-942. https://doi.org/10.1111/j.1467-9892.2006.00496.x
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  7. Yoon, J. E. and Hwang, S. Y. (2015) Integer-valued GARCH models for count time series: Case study, Korean Journal of Applied Statistics, 28, 115-122. https://doi.org/10.5351/KJAS.2015.28.1.115
  8. Zhu, F. (2011). A negative binomial integer-valued GARCH model, Journal of Time Series Analysis, 32, 54-67. https://doi.org/10.1111/j.1467-9892.2010.00684.x
  9. Zhu, F. (2012). Zero-in ated Poisson and negative binomial integer-valued GARCH models, Journal of Statistical Planning and Inference, 142, 826-839. https://doi.org/10.1016/j.jspi.2011.10.002

피인용 문헌

  1. A recent overview on financial and special time series models vol.29, pp.1, 2016, https://doi.org/10.5351/KJAS.2016.29.1.001