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    Title: 結合機器學習與混合頻率方法即時預測美國通膨率
    Nowcasting of U.S. Inflation Rates Using Machine Learning and Mixed-Frequency Approaches
    Authors: 謝錚奇
    HSIEH, CHENG-CHI
    Contributors: 林馨怡
    Lin, Hsin-Yi
    謝錚奇
    HSIEH, CHENG-CHI
    Keywords: 通膨率
    狀態空間模型
    LASSO
    MIDAS
    Date: 2024
    Issue Date: 2024-08-05 13:36:10 (UTC+8)
    Abstract: 本論文使用狀態空間模型以及 sparse group LASSO MIDAS (sg-LASSO- MIDAS) 模型,即時預測預測美國 1996 年 5 月至 2023 年 12 月的通貨膨脹 率。實證結果顯示,使用高頻變數有助於提升美國通貨膨脹率的預測準確性,其 中 sg-LASSO-MIDAS 藉由對稀疏組的係數估計限制,將 28 筆日資料視為同一組別,並在係數估計時,對同一組別的係數估計進行相同限制,能更好的利用經狀態空間模型處理過後的高頻資料變數做出通膨預測,在本論文的五個預測期間預測結果比較中,取得最好的預測表現。
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    Description: 碩士
    國立政治大學
    經濟學系
    111258003
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111258003
    Data Type: thesis
    Appears in Collections:[經濟學系] 學位論文

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