How to make plausibility-based forecasting more accurate
© Springer International Publishing AG 2017. In recent papers, a new plausibility-based forecasting method was proposed. While this method has been empirically successful, one of its steps—selecting a uniform probability distribution for the plausibility level—is heuristic. It is therefore desirable...
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| Main Authors: | , , |
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| Format: | Book Series |
| Published: |
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012273390&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/40813 |
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| Institution: | Chiang Mai University |
| Summary: | © Springer International Publishing AG 2017. In recent papers, a new plausibility-based forecasting method was proposed. While this method has been empirically successful, one of its steps—selecting a uniform probability distribution for the plausibility level—is heuristic. It is therefore desirable to check whether this selection is optimal or whether a modified selection would like to a more accurate forecast. In this paper, we show that the uniform distribution does not always lead to (asymptotically) optimal estimates, and we show how to modify the uniform-distribution step so that the resulting estimates become asymptotically optimal. |
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