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dc.contributor.authorChang, SCen_US
dc.contributor.authorLai, HCen_US
dc.contributor.authorYu, HCen_US
dc.date.accessioned2014-12-08T15:20:16Z-
dc.date.available2014-12-08T15:20:16Z-
dc.date.issued2005-06-01en_US
dc.identifier.issn0040-1625en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.techfore.2003.09.002en_US
dc.identifier.urihttp://hdl.handle.net/11536/14402-
dc.description.abstractThe semiconductor industry plays an important role in Taiwan's economy. In this paper, we constructed a rolling Grey forecasting model (RGM) to predict Taiwan's annual semiconductor production. The univariate Grey forecasting model (GM) makes forecast of a time series of data without considering possible correlation with any leading indicators. Interestingly, within the RGM there is a constant, P value, which was customarily set to 0.5. We hypothesized that making the P value a variable of time could generate more accurate forecasts. It was expected that the annual semiconductor production in Taiwan should be closely tied with U.S. demand. Hence, we let the P value be determined by the yearly percent change in real gross domestic product (GDP) by U.S. manufacturing industry. This variable P value RGM generated better forecasts than the fixed P value RGM. Nevertheless, the yearly percent change in real GDP by U.S. manufacturing industry is reported after a year ends. It cannot serve as a leading indicator for the same year's U.S. demand. We found out that the correlation between the yearly survey of anticipated industrial production growth rates in Taiwan and the yearly percent changes in real GDP by U.S. manufacturing industry has a correlation coefficient of 0.96. Therefore, we used the former to determine the P value in the RGM, which generated very accurate forecasts. (c) 2003 Elsevier Inc. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectgrey forecastingen_US
dc.subjectRGMen_US
dc.subjectsemiconductor industryen_US
dc.titleA variable P value rolling Grey forecasting model for Taiwan semiconductor industry productionen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.techfore.2003.09.002en_US
dc.identifier.journalTECHNOLOGICAL FORECASTING AND SOCIAL CHANGEen_US
dc.citation.volume72en_US
dc.citation.issue5en_US
dc.citation.spage623en_US
dc.citation.epage640en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000229172200008-
dc.citation.woscount43-
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