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일별 시계열을 이용한 월별 시계열의 계절조정
Seasonal adjustment for monthly time series based on daily time series
Korean J Appl Stat 2023;36(5):457-471
Published online October 31, 2023
© 2023 The Korean Statistical Society.

Geung-Hee Lee1,a

a한국방송통신대학교 통계·데이터과학과

aDepartment of Data Science and Statistics, Korea National Open University
1Department of Data Science and Statistics, Korea National Open University, 86 Daehak-ro, Jongno-Gu, Seoul 03087, Korea. E-mail:
The work was supported by the Korea National Open University Research Fund in 2021.
Received March 23, 2023; Revised April 18, 2023; Accepted April 18, 2023.
월별 시계열은 일별 시계열의 월별 합이지만, 일별 시계열을 대체로 관측할 수 없어서 요일구성변동, 명절·공휴일변동 등 달력변동을 가상적으로 가정한 가변수를 포함한 RegARIMIA 모형을 이용하여 추정하고 있다. 일별 시계열을 관측할 수 있다면 요일구성변동, 명절·공휴일변동 등 달력변동을 일별 시계열을 바탕으로 추정할 수 있고 이를 이용하여 월별 시계열의 계절조정을 개선할 수 있다. 이 논문에서는 일별 시계열의 달력변동 추정을 이용하여 월별 시계열의 계절조정을 개선하는 방법을 제안하고, 이 방법을 적용하여 3개의 월별 시계열을 계절조정하고 기존의 X-13ARIMA-SEATS를 이용한 계절조정과 비교하였다.
The monthly series is an aggregation of daily values. In the absence of observable daily data, calendar effects such as trading day and holidays are estimated using a RegARIMA model. However, if the daily series were observable, these calendar effects could be estimated directly from the daily series, potentially improving the seasonal adjustment of the monthly time series. In this paper, we propose a method to improve the seasonal adjustment of monthly time series by using calendar variation estimation based on daily time series. We apply this seasonal adjustment method to three monthly time series and compare our results with those obtained using X-13ARIMA-SEATS.
주요어 : 계절조정, 달력변동, 일별 시계열, 월별 시계열, X-13ARIMA-SEATS
Keywords : daily time series, monthly time series, calendar variation, seasonal adjustment, X-13ARIMA-SEATS
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