Distribution Based Level Change Detection in a Random Level Forecasting Model랜덤 수준 예측 모형에서 분포기반의 수준변화 인식

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It is well known that an unexpected level change in time series can cause persistent forecasting errors, depending on the change size and the underlying time series process. This relationship is demonstrated particularly with macroeconomic and financial time series. Forecasting literature suggests using the relevant test statistics to detect the level change, but they are just measures that are not coupled with the correct statistical distributions. Hence, this study aims to find the correct statistical distribution of the level change statistic and to adapt the forecasting equation accordingly. The performance of the proposed method is validated by simulated time series and two empirical examples.
Publisher
대한산업공학회
Issue Date
2017-08
Language
English
Keywords

Level Change; State Space Model; Statistical Distribution; Adaptive Forecasting

Citation

대한산업공학회지, v.43, no.4, pp.255 - 263

ISSN
1225-0988
DOI
10.7232/JKIIE.2017.43.4.255
URI
http://hdl.handle.net/10203/241240
Appears in Collection
MT-Journal Papers(저널논문)
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