• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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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.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.321-328
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• 2000

Working day variations caused by trading days, holidays and leap year make it difficult to analyze monthly or quarterly economic time series. In this paper, we adjust working day variations from economic time series via RegARIMA model.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.349-360
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• 2013

Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.

• 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.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.139-144
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• 2002

계절변동조정방법인 X-12-ARIMA방법을 이용할 때에는 우리 실정에 적합한 옵션을 선택하고, 우리만에 특수한 명절과 조업일수영향을 사전에 조정해야한다. 본고에서는 명절과 조업일수영향을 측정하는 모형을 설정하고, 이것으로 추정된 사전조정요인을 원계열에서 제거했을 때 계절변동 및 계절변동조정계열의 안정성이 향상되었는가를 진단하고, 분류별로 적합한 X-12-ARIMA방법의 옵션을 제안하였다.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.161-171
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• 2019

Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.457-471
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• 2023

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.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.225-236
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• 2000

To compile seasonally-adjusted statistics for Korean economic statistics accurately. it is necessary to develop a Korean seasonal adjustment program. In this paper. the Korean seasonal adjustment program BOK-X-12-ARIMA, developed through modification of the US. Bureau of the Census's X-12-ARIT\IA, is explained in detail.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.387-387
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• 1999

The traditional holiday, Sol which is based on a lunar calendar, falls in January orFebruary and makes it difficult to analyze time series data accurately. To analyze whetherSol raises consumer prices or not, RegARIMA models and paired t tests are used. It isfound that Sol raises consumer prices of food products significantly, but So1's effects onconsumer prices of all items are not significant.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.357-395
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• 1999

음력설이 다가오면서 과일, 채소류 등 식료품물가가 통상 크게 오름에 따라 매년 초에 물가상승과 이로 인한 경제적 부작용을 우려하는 논의가 있어 왔다. 이를 고려하여 본고에서는 음력설이 소비자물가에 미치는 영향을 RegARIMA모형과 쌍체검정을 이용하여 분석하고 그 시사점을 찾아보았다. 분석결과 음력설은 농수축산물을 중심으로 식료품 가격의 변동성을 확대시켜 체감물가를 높임으로써 물가불안심리를 유발하는 측면이 있으나 전체 소비자물가에 미치는 영향은 크지 않은 것으로 나타났다. 음력설을 앞두고 발생하는 이러한 식료품 중심의 물가상승은 제수용품 등을 중심으로 일시적 수요증가에 주도되는 구조적인 측면이 크므로 이를 완화하는 방안을 강구하여 물가불안심리와 경제적 부작용을 줄일 필요가 있다.