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VaR forecasting for PM10 data using time series models
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- Authors
- Advisor
- 이상열
- Major
- 자연과학대학 통계학과
- Issue Date
- 2016-08
- Publisher
- 서울대학교 대학원
- Keywords
- ARMA model ; goodness of fit test ; PM10 ; quantile regression ; VaR forecasting
- Description
- 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2016. 8. 이상열.
- Abstract
- This thesis analyze the particular matter (PM)-10 data in Korea using time series analysis. To this task, we use the log-transformed data of the daily averages of the PM10 values which collected from Korea Meteorological Administration to obtain an optimal ARMA model. Then, we conduct the entropy-based goodness of fit (GOF) test for the fitted residuals to check the departure from the normal and skew-t distributions, and also a conditional value-at-risk (VaR) forecasting using the parametric and quantile regression methods. The obtained result has a potential usage as a guideline for the patients with some respiratory disease to pay more attention to health care when the conditional VaR forecast goes beyond the limit values of severe health hazards.
- Language
- English
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