응용통계연구 (The Korean Journal of Applied Statistics)

  • 제33권6호
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  • Pages.723-738
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  • 2020
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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Adaptive lasso를 이용하여 추세-정상시계열과 차분-정상시계열을 판별하는 방법에 대한 연구

Discrimination between trend and difference stationary processes based on adaptive lasso

  • 나옥경 (경기대학교 응용통계학과)
  • Na, Okyoung (Department of Applied Statistics, Kyonggi University)
  • 투고 : 2020.10.14
  • 심사 : 2020.10.17
  • 발행 : 2020.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 추세-정상시계열과 차분-정상시계열을 판별하는 방법에 대해 연구한다. 두 시계열 모형은 시계열적 특징, 충격의 지속성 여부, 시계열을 정상화시키는 방법 등이 모두 다르므로, 어떤 모형을 선택하냐에 따라 분석 방법이나 해석에 차이가 발생한다. 따라서 시계열 자료를 분석할 때 추세-정상성과 차분-정상성을 판별하는 것은 매우 중요한 일이다. 두 시계열을 구분하는 중요한 기준은 단위근의 존재 여부이므로, 단위근 검정 결과를 활용할 수 있다. 최근 연구 결과들을 살펴보면, 다양한 시계열 모형을 적합시킬 때 뿐만 아니라 비정상 자기회귀모형의 차분 차수를 결정할 때도 adaptive lasso와 같은 벌점화 추정방법을 도입, 사용하고 있다. 본 논문에서도 adaptive lasso를 이용하여 추세-정상시계열과 차분-정상시계열을 판별하는 방법을 제안, 연구를 진행하였다. 단위근 검정을 이용한 분류 방법과 adaptive lasso 추정량을 기초로 한 분류 방법에 대한 비교 모의실험을 수행하였고, 그 결과 추세-정상시계열이 참인 경우는 adaptive lasso 방법의 분류 정확도가 단위근 검정방법보다 좀 더 우세하며, 차분-정상시계열의 경우에는 반대로 정확도가 떨어지는 것을 확인할 수 있었다.

In this paper, we study a method to discriminate between trend stationary and difference stationary processes. Since a crucial ingredient of this discrimination is to determine the existence of unit root, we can use a unit root testing strategy. So, we introduce a discrimination based on unit root testing and propose the method using the adaptive lasso. Our Monte Carlo simulation experiments show that the adaptive lasso improves the discrimination accuracy when the process is trend stationary, but has lower accuracy than unit root strategy where the process is difference stationary.

키워드

참고문헌

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