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dc.contributor.author신동완*
dc.date.accessioned2020-08-24T16:30:18Z-
dc.date.available2020-08-24T16:30:18Z-
dc.date.issued2019*
dc.identifier.issn2287-7843*
dc.identifier.otherOAK-27753*
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/255209-
dc.description.abstractForecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by Mc- Cracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts. © 2019 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.*
dc.languageEnglish*
dc.publisherKorean Statistical Society*
dc.subjectDeep neural network*
dc.subjectDifferencing*
dc.subjectDimension reduction*
dc.subjectEmployment forecast*
dc.subjectGated recurrent unit*
dc.subjectLong short term memory*
dc.titleThe roles of differencing and dimension reduction in machine learning forecasting of employment level using the FRED big data*
dc.typeArticle*
dc.relation.issue5*
dc.relation.volume26*
dc.relation.indexSCOPUS*
dc.relation.indexKCI*
dc.relation.startpage497*
dc.relation.lastpage506*
dc.relation.journaltitleCommunications for Statistical Applications and Methods*
dc.identifier.doi10.29220/CSAM.2019.26.5.497*
dc.identifier.scopusid2-s2.0-85074648562*
dc.author.googleChoi J.-E.*
dc.author.googleShin D.W.*
dc.contributor.scopusid신동완(7403352539)*
dc.date.modifydate20240116115756*
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자연과학대학 > 통계학전공 > Journal papers
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