전이함수모형을 이용한 약품비 지출의 예측Forecasting drug expenditure with transfer function model
- Authors
- 박미혜; 임민성; 성병찬
- Issue Date
- Apr-2018
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- drug expenditure; drug price reduction; medical expenses in health insurance; transfer function model; autoregressive error model
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.31, no.2, pp 303 - 313
- Pages
- 11
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 31
- Number
- 2
- Start Page
- 303
- End Page
- 313
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/2305
- DOI
- 10.5351/KJAS.2018.31.2.303
- ISSN
- 1225-066X
2383-5818
- Abstract
- 본 논문에서는 약품비 지출에 대한 예측을 수행하기 위하여 시계열 모형을 도입한다. 2012년 약가 일괄인하를반영하기 위하여 구간별 모형을 토대로, 자기회귀오차모형과전이함수모형을 고려하였다. 자기회귀오차모형에서는 예측의 편리성을 위하여 결정적 추세만을 고려하였으며, 전이함수모형에서는 주요한 외생변수와의 교차상관성을 이용하여 약품비지출의 인과 메커니즘을 설명하였다. 각 모형에서 약가 일괄인하 이후 수준 변화가 유의하게 나타났으며, 전이함수모형에서는 의약품 사용자 수 및 노인환자 비중 시계열 변수가유의하게 나타났다. 자기회귀오차모형은 약가 일괄인하로 의한 약품비 수준이동에 좌우되어 비교적 낮은 예측값이도출되었으며, 전이함수모형은 약품비 지출에 영향을 미치는 외부설명변수의 증가 추세가 적절히 반영되어 더 높은 예측값을 보였다. 설명변수를 포함하지 않을 경우, 약품비수준이동만을 고려한 ARIMA 모형은 약품비 지출 추세를 가장높이 예측하였다.
This study considers time series models to forecast drug expenditures in national health insurance. We adopt autoregressive error model (ARE) and transfer function model (TFM) with segmented level and trends (before and after 2012) in order to reflect drug price reduction in 2012. The ARE has only a segmented deterministic term to increase the forecasting performance, while the TFM explains a causality mechanism of drug expenditure with closely related exogenous variables.
The mechanism is developed by cross-correlations of drug expenditures and exogenous variables. In both models, the level change appears significant and the number of drug users and ratio of elderly patients variables are significant in the TFM. The ARE tends to produce relatively low forecasts that have been influenced by a drug price reduction; however, the TFM does relatively high forecasts that have appropriately reflected the effects of exogenous variables. The ARIMA model without the exogenous variables produce the highest forecasts.
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