• Title/Summary/Keyword: 명절효과

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RegARIMA 모형을 이용한 음력 명절효과의 검정에 관한 연구

  • Mun, Gwon-Sun
    • Proceedings of the Korean Statistical Society Conference
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    • pp.73-77
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    • 2005
  • 본 논문은 시계열에 내재된 설${\cdot}$추석 등 음력 명절효과의 존재를 검정하기 위해 RegARIMA 모형의 잔차에 대한 t-검정 통계량을 제시하였으며 Box-plot에 의한 그래프적 진단을 시도하였다. 제시된 t-검정 결과를 X-12-ARIMA의 AICC-사전검정 및 RegARIMA 모형에 의해 추정된 명절효과 회귀계수의 t-값과 비교하였다. 사용된 명절효과 변수는 Bell과 Hillmer(1983)의 명절효과 변수이다.

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Study on the National Holiday Effect of B2C Online Mobile Phone Market in China (중국 B2C 온라인 핸드폰 판매량의 명절효과에 대한 연구)

  • Hong, Jaewon;Kwak, Youngsik;Nam, Yongsik;Nam, Yoonjung
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.189-190
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    • 2019
  • 본 연구에서는 명절이 소비자들의 제품 교체주기에 영향을 미치는지를 탐색하기 위해 중국 온라인 휴대폰 판매 데이터를 바탕으로 명절 시기별 구매간격 차이검증을 통해 실증적으로 분석하고자 하였다. 분석결과, 명절시점과 평상시점 간에 제품 구매간격의 차이가 있는 것으로 나타났다. 즉, 명절시점이 평상시점보다 제품 교체주기가 짧은 것으로 나타나 명절효과가 있음을 증명하였다. 한편, 춘절, 노동절, 국경절 등 명절유형 간 제품 구매간격의 차이는 나타나지 않았다. 이는 기업이 명절시점을 활용하여 교체 가능성이 높은 소비자를 선별적으로 공략한다면 더 높은 성과를 얻을 수 있음을 시사한다. 본 연구의 결과는 실무적으로 볼 때 기업으로 하여금 교체확률이 높은 시기에 마케팅을 집중할 수 있도록 할 수 있을 것이며, 학술적으로는 제품 구매 간격에 영향을 미치는 구매시점 효과 중 명절효과를 온라인상의 고관여 제품에 적용하여 탐구했다는 공헌점이 있다.

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A Study for Shapes of Filter on the Prior Adjustment of the Holiday Effect (명절효과 사전조정을 위한 파급유형에 관한 연구)

  • Kim, Kee-Whan;Shin, Hyun-Gyu
    • The Korean Journal of Applied Statistics
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    • v.23 no.2
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    • pp.275-284
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    • 2010
  • In this study, we introduce filters that used for the prior adjustment of the holiday effect in seasonal adjustment. And we propose new filters having more various and flexible patterns than conventional ones. Under the practical assumption that patterns of effects before and after the holiday are different, we compare adjustment effect of the proposed filters and the existing ones. In comparison study, we estimate the effect from all possible combinations of shapes of filter by RegARIMA. And then, to adjust holiday effect, we apply the estimated results to time series data of industrial production and shipment index data in South Korea.

X11ARIMA Procedure (한국형 X11ARIMA 프로시져에 관한 연구)

  • 박유성;최현희
    • The Korean Journal of Applied Statistics
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    • v.11 no.2
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    • pp.335-350
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    • 1998
  • X11ARIMA is established on the basis of X11 which is one of smoothing approach in time series area and this procedure was introduced by Bureau of Census of United States and developed by Dagum(1975). This procedure had been updated and adjusted by Dagum(1988) with 174 economic index of North America and has been used until nowadays. Recently, X12ARIMA procedure has been studied by William Bell et.al. (1995) and Chen. & Findly(1995) whose approaches adapt adjusting outliers, Trend-change effects, seasonal effect, arid Calender effect. However, both of these procedures were implemented for correct adjusting the economic index of North America. This article starts with providing some appropriate and effective ARIMA model for 102 indexes produced by national statistical office in Korea; which consists of production(21), shipping(27), stock(27), and operating rate index(21). And a reasonable smoothing method will be proposed to reflect the specificity of Korean economy using several moving average model. In addition, Sulnal(lunar happy new year) and Chusuk effects will be extracted from the indexes above and both of effects reflect contribution of lunar calender effect. Finally, we will discuss an alternative way to estimate holiday effect which is similar to X12ARIMA procedure in concept of using both of ARIMA model and Regression model for the best fitness.

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A Development of Time-Series Model for City Gas Demand Forecasting (도시가스 수요량 예측을 위한 시계열 모형 개발)

  • Choi, Bo-Seung;Kang, Hyun-Cheol;Lee, Kyung-Yun;Han, Sang-Tae
    • The Korean Journal of Applied Statistics
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    • v.22 no.5
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    • pp.1019-1032
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    • 2009
  • The city gas demand data has strong seasonality. Thus, the seasonality factor is the majority for the development of forecasting model for city gas supply amounts. Also, real city gas demand amounts can be affected by other factors; weekday effect, holiday effect, the number of validity day, and the number of consumptions. We examined the degree of effective power of these factors for the city gas demand and proposed a time-series model for efficient forecasting of city gas supply. We utilize the liner regression model with autoregressive regression errors and we have excellent forecasting results using real data.