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題名 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
作者 謝錚奇
HSIEH, CHENG-CHI
貢獻者 林馨怡
Lin, Hsin-Yi
謝錚奇
HSIEH, CHENG-CHI
關鍵詞 通膨率
狀態空間模型
LASSO
MIDAS
日期 2024
上傳時間 5-Aug-2024 13:36:10 (UTC+8)
摘要 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。
參考文獻 Atkeson, A., Ohanian, L. E., 2001. Are Phillips curves useful for forecasting inflation? Federal Reserve bank of Minneapolis quarterly review, 25(1), 2–11. Babii, A., Ghysels, E., Striaukas, J., 2022. Machine learning time series regressions with an application to nowcasting. Journal of Business & Economic Statistics, 40(3), 1094–1106. Barbaglia, L., Consoli, S., Manzan, S., 2023. Forecasting with economic news. Journal of Business & Economic Statistics, 41(3), 708–719. Breitung, J. O., Roling, C., 2015. Forecasting inflation rates using daily data: A nonparametric MIDAS approach. Journal of Forecasting, 34(7), 588–603. Bybee, L., Kelly, B. T., Manela, A., Xiu, D., 2020. The structure of economic news. Cavallo, A., 2013. Online and official price indexes: Measuring Argentina’s inflation. Journal of Monetary Economics, 60(2), 152–165. Cavallo, A., Rigobon, R., 2016. The billion prices project: Using online prices for measurement and research. Journal of Economic Perspectives, 30(2), 151–178. Durbin, J., Koopman, S. J., 2012. Time series analysis by state space methods. Foroni, C., Marcellino, M., Schumacher, C., 2015. Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society Series A: Statistics in Society, 178(1), 57–82. Funke, M., Mehrotra, A., Yu, H., 2015. Tracking Chinese CPI inflation in real time. Empirical Economics, 48, 1619–1641. Ghysels, E., 2016. MIDAS matlab toolbox. URL: http://www.unc.edu/~eghysels/papers/MIDAS_Usersguide_V1.0.pdf.Lastaccessedon, 8(16), 2016. Ghysels, E., Santa-Clara, P., Valkanov, R., 2004. The MIDAS touch: Mixed data sampling regression models. Ghysels, E., Sinko, A., Valkanov, R., 2007. MIDAS regressions: Further results and new directions. Econometric reviews, 26(1), 53–90. Giannone, D., Reichlin, L., Small, D., 2008. Nowcasting: The real-time informational content of macroeconomic data. Journal of monetary economics, 55(4), 665–676. Knotek II, E. S., Zaman, S., 2017. Nowcasting US headline and core inflation. Journal of Money Credit and Banking, 49(5), 931–968. Medeiros, M. C., Vasconcelos, G. F. R., Veiga, A., Zilberman, E., 2021. Forecasting inflation in a data-rich environment: the benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98–119. Monteforte, L., Moretti, G., 2013. Real-time forecasts of inflation: The role of financial variables. Journal of Forecasting, 32(1), 51–61. Schorfheide, F., Song, D., 2015. Real-time forecasting with a mixed-frequency VAR. Journal of Business & Economic Statistics, 33(3), 366–380. Steindel, C., Cecchetti, S. G., Chu, R., 2005. The unreliability of inflation indicators. Available at SSRN 716681. Stock, J. H., Watson, M. W., 1999. Forecasting inflation. Journal of monetary economics, 44(2), 293–335. Torrontegui, E., Ibáñez, S., Martínez-Garaot, S., Modugno, M., del Campo, A., Guéry-Odelin, D., Ruschhaupt, A., Chen, X., Muga, J. G., 2013. Shortcuts to adiabaticity. Zheng, T., Fan, X., Jin, W., Fang, K., 2024. Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data. International Journal of Forecasting, 40(2), 746–761.
描述 碩士
國立政治大學
經濟學系
111258003
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111258003
資料類型 thesis
dc.contributor.advisor 林馨怡zh_TW
dc.contributor.advisor Lin, Hsin-Yien_US
dc.contributor.author (Authors) 謝錚奇zh_TW
dc.contributor.author (Authors) HSIEH, CHENG-CHIen_US
dc.creator (作者) 謝錚奇zh_TW
dc.creator (作者) HSIEH, CHENG-CHIen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 13:36:10 (UTC+8)-
dc.date.available 5-Aug-2024 13:36:10 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 13:36:10 (UTC+8)-
dc.identifier (Other Identifiers) G0111258003en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152697-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經濟學系zh_TW
dc.description (描述) 111258003zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.tableofcontents 1 緒論 1 2 文獻回顧 3 3 研究方法 7 3.1 狀態空間模型 7 3.2 MIDAS 12 3.3 sg-LASSO 16 4 資料 19 4.1 通貨膨脹率資料 19 4.2 預測變數資料 20 4.3 樣本外預測能力 22 5 實證結果 24 5.1 通膨年增率預測結果 24 5.2 通膨月增率 27 5.3 設定資料滾動區間 31 5.4 解釋變數選取 33 5.5 調整滾動視窗長度和通膨起伏 36 6 結論 40 參考文獻 41zh_TW
dc.format.extent 843023 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111258003en_US
dc.subject (關鍵詞) 通膨率zh_TW
dc.subject (關鍵詞) 狀態空間模型zh_TW
dc.subject (關鍵詞) LASSOzh_TW
dc.subject (關鍵詞) MIDASzh_TW
dc.title (題名) 結合機器學習與混合頻率方法即時預測美國通膨率zh_TW
dc.title (題名) Nowcasting of U.S. Inflation Rates Using Machine Learning and Mixed-Frequency Approachesen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Atkeson, A., Ohanian, L. E., 2001. Are Phillips curves useful for forecasting inflation? Federal Reserve bank of Minneapolis quarterly review, 25(1), 2–11. Babii, A., Ghysels, E., Striaukas, J., 2022. Machine learning time series regressions with an application to nowcasting. Journal of Business & Economic Statistics, 40(3), 1094–1106. Barbaglia, L., Consoli, S., Manzan, S., 2023. Forecasting with economic news. Journal of Business & Economic Statistics, 41(3), 708–719. Breitung, J. O., Roling, C., 2015. Forecasting inflation rates using daily data: A nonparametric MIDAS approach. Journal of Forecasting, 34(7), 588–603. Bybee, L., Kelly, B. T., Manela, A., Xiu, D., 2020. The structure of economic news. Cavallo, A., 2013. Online and official price indexes: Measuring Argentina’s inflation. Journal of Monetary Economics, 60(2), 152–165. Cavallo, A., Rigobon, R., 2016. The billion prices project: Using online prices for measurement and research. Journal of Economic Perspectives, 30(2), 151–178. Durbin, J., Koopman, S. J., 2012. Time series analysis by state space methods. Foroni, C., Marcellino, M., Schumacher, C., 2015. Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society Series A: Statistics in Society, 178(1), 57–82. Funke, M., Mehrotra, A., Yu, H., 2015. Tracking Chinese CPI inflation in real time. Empirical Economics, 48, 1619–1641. Ghysels, E., 2016. MIDAS matlab toolbox. URL: http://www.unc.edu/~eghysels/papers/MIDAS_Usersguide_V1.0.pdf.Lastaccessedon, 8(16), 2016. Ghysels, E., Santa-Clara, P., Valkanov, R., 2004. The MIDAS touch: Mixed data sampling regression models. Ghysels, E., Sinko, A., Valkanov, R., 2007. MIDAS regressions: Further results and new directions. Econometric reviews, 26(1), 53–90. Giannone, D., Reichlin, L., Small, D., 2008. Nowcasting: The real-time informational content of macroeconomic data. Journal of monetary economics, 55(4), 665–676. Knotek II, E. S., Zaman, S., 2017. Nowcasting US headline and core inflation. Journal of Money Credit and Banking, 49(5), 931–968. Medeiros, M. C., Vasconcelos, G. F. R., Veiga, A., Zilberman, E., 2021. Forecasting inflation in a data-rich environment: the benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98–119. Monteforte, L., Moretti, G., 2013. Real-time forecasts of inflation: The role of financial variables. Journal of Forecasting, 32(1), 51–61. Schorfheide, F., Song, D., 2015. Real-time forecasting with a mixed-frequency VAR. Journal of Business & Economic Statistics, 33(3), 366–380. Steindel, C., Cecchetti, S. G., Chu, R., 2005. The unreliability of inflation indicators. Available at SSRN 716681. Stock, J. H., Watson, M. W., 1999. Forecasting inflation. Journal of monetary economics, 44(2), 293–335. Torrontegui, E., Ibáñez, S., Martínez-Garaot, S., Modugno, M., del Campo, A., Guéry-Odelin, D., Ruschhaupt, A., Chen, X., Muga, J. G., 2013. Shortcuts to adiabaticity. Zheng, T., Fan, X., Jin, W., Fang, K., 2024. Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data. International Journal of Forecasting, 40(2), 746–761.zh_TW