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https://ah.lib.nccu.edu.tw/handle/140.119/99637| 題名: | 大數據預測通貨膨脹率 Forecasting Inflation with Big Data |
作者: | 廖珈燕 Liao, Jia Yan |
貢獻者: | 林馨怡 Lin, Hsin Yi 廖珈燕 Liao, Jia Yan |
關鍵詞: | Google trends 關鍵字 通貨膨脹率 Google trends Inflation |
日期: | 2016 | 上傳時間: | 3-Aug-2016 | 摘要: | 本文主要是透過 Google trends 網站提供的關鍵字搜尋量資料，\n探討網路資料是否能夠提供通貨膨脹率的即時資訊。\n透過美國消費者物價指數的組成細項作為依據，蒐集美國2004年1月至2015年12月的 Google trends 關鍵字變數，並藉由最小絕對壓縮挑選機制(Least absolute shrinkage and selection operator)、\n彈性網絡(Elastic Net)以及主成分分析法(Principal component analysis)等等變數挑選機制，有效地整合大量的關鍵字資料。實證結果發現，透過適當變數挑選後的 Google trends 關鍵字變數確實可改善美國通貨膨脹率的即時預測表現，並為美國通貨膨脹率提供額外有效的資訊。此外，我們透過台灣的關鍵字資料檢驗，也確認Google trends 關鍵字資料可以幫助台灣通貨膨脹率的即時預測。 | 參考文獻: | Ang, A., Bekaert, G., Wei, M. 2007. \n Do macro variables, asset markets, or surveys forecast inflation better? \n Journal of Monetary Economics , 54(4), 1163--1212. \n\n \nAskitas, N., Zimmermann, K. 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資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0103258016 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
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