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題名 美國貨幣政策的國際外溢效果：結合局部投影法和混合頻率模型之應用
International Spillover Effects of U.S. Monetary Policy Shocks: Combining the Local Projections and MIDAS Approach
作者 李郁涵
Lee, Yu-Han
貢獻者 林馨怡
Lin, Hsin-Yi
李郁涵
Lee, Yu-Han
關鍵詞 混合頻率
貨幣政策
外溢效果
日期 2025
上傳時間 1-Jul-2025 15:34:15 (UTC+8)
摘要 本文以MIDAS結合局部投影法，在有效運用不同頻率數據的資訊，並降低模型錯誤設置對估計結果的影響下，探討美國聯準會貨幣政策的國際外溢效果。以1988年第三季至2023年第三季的實證結果顯示，聯準會升息後，多數國家的實質產出成長率衝擊反應函數趨勢與美國相似，且受衝擊程度與美國相當，或甚至高於美國影響，此代表美國貨幣政策在全球景氣循環中具關鍵影響力，亦突顯關注外溢效果的必要性。而美國升息初期，產出上升之現象，可能與貨幣政策所蘊含的資訊效果有關。此外，本文比較有無混頻的估計結果，發現單一頻率模型的衝擊反應函數較平緩，顯示若未考慮混頻，將可能低估各國所受之擊。
參考文獻 Bacchiocchi, E., Bastianin, A., Missale, A., and Rossi, E. (2016). Structural Analysis with Mixed Frequencies: Monetary Policy, Uncertainty and Gross Capital Flows. JRC Working Papers in Economics and Finance, 2016/4. Beirne, J., Renzhi, N., and Volz, U. (2023). When the United States and the People＇s Republic of China Sneeze: Monetary Policy Spillovers to Asian Economies. Open Economies Review, 34, 519–540. Bluwstein, K., and Canova, F. (2016). Beggar-Thy-Neighbor? The International Effects of ECB Unconventional Monetary Policy Measures. International Journal of Central Banking, 12(3), 69–120. Bonser-Neal, C., and Roley, V.V., and Sellon, G.H., Jr. (1998). Monetary Policy Actions, Intervention, and Exchange Rates: A Reexamination of the Empirical Relationships Using Federal Funds Rate Target Data. The Journal of Business, 71(2), 147–177. Breitenlechner, M., Georgiadis, G., and Schumann, B. (2022). What Goes Around Comes Around: How Large are Spillbacks from US Monetary Policy? Journal of Monetary Economics, 131, 45–60. Durdu, C.B., and Martin, A., and Zer, I. (2019). The Role of U.S. Monetary Policy in Global Banking Crises. Finance and Economics Discussion Series, 2019(39). Bu, C., and Rogers, J., and Wu, W. (2021). A unified measure of Fed monetary policy shocks. Journal of Monetary Economics, 118, 331–349. Federal Reserve Bank of Philadelphia (2025/1/30). Compilation, Tealbook (formerly Greenbook) Data Sets. Federal Reserve Bank of Philadelphia. Feldkircher, M., Huber, F., and Pfarrhofer, M. (2021). Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession. Scottish Journal of Political Economy, 68, 287–297. Ferrara, L., and Guérin, P. (2018). What are the Macroeconomic Effects of High-Frequency uncertainty shocks? Journal of Applied Econometrics, 33(5), 662–679. Foroni, C., and Marcellino, M. (2014). Mixed-Frequency Structural Models:Identification, Estimation, and Policy Analysis. Journal of Applied Econometrics, 29, 1118–1144. Francis, N., Ghysels , E., and Owyang, MT. (2011). The Low-Frequency Impact of Daily Monetary Policy Shocks. Federal Reserve Bank of St. Louis Working Paper, 2011(9). Georgiadis, G. (2016). Determinants of Global Spillovers from US Monetary Policy. Journal of International Money and Finance, 67, 41–61. Ghysels, E., Santa-Clara, P., and Valkanov, R. (2004). The MIDAS Touch: Mixed Data Sampling Regression Models. CIRANO Working Papers, 2004(20). Ghysels, E., Sinko, A., and Valkanov, R. (2007). MIDAS Regressions: Further Results and New Directions. Econometric Reviews, 26(1), 53–90. Ghysels, E., Kvedaras, V., and Zemlys, V. (2016). Mixed Frequency Data Sampling Regression Models: The R Package midasr. Journal of Statistical Software, 72(4), 1–35. Ghysels, E., Kvedaras, V., and Zemlys, V. (2020). Mixed Data Sampling (MIDAS) Regression Models. Handbook of Statistics, 42, 117–153. Jarociński, M., and Karadi, P. (2020). Deconstructing Monetary Policy Surprises—The Role of Information Shocks. American Economic Journal: Macroeconomics, 12(2), 1–43. Jordà, Ò. (2023). Local Projections for Applied Economics. Federal Reserve Bank of San Francisco Working Paper, 2023(16). Lastauskas, P., and Nguyen, A.D.M. (2023). Global Impacts of US Monetary Policy Uncertainty Shocks. Journal of International Economics, 145. Lin (2025). Real Time Data and Policy Evaluation. Working Paper. Marcellino, M., and Sivec, V. (2016). Monetary, Fiscal and Oil Shocks: Evidence based on Mixed Frequency Structural FAVARs. Journal of Econometrics, 193, 335–348. McCracken, M.W., Owyang , M., and Sekhposyan, T. (2015). Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR. Federal Reserve Bank of St. Louis Working Paper, 2015(30). Mohaddes, K., and Raissi, M. (2020). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2019Q4 University of Cambridge: Judge Business School (mimeo). Mohaddes, K., and Raissi, M. (2024). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2023Q3. University of Cambridge: Judge Business School (mimeo). Romer, C.D., and Romer, D.H. (2004). A New Measure of Monetary Shocks: Derivation and Implications. American Economic Review, 94(4), 1055–1084. Rooj, D., and Sengupta, R. (2018). Monetary Policy and Private Investment in India: The MIDAS Experience. Advances in Finance & Applied Economics, 119–131. Schumacher, C. (2016). A Comparison of MIDAS and Bridge Equations. International Journal of Forecasting, 32(2), 257–270. Ugazio, G., and Xin, W. (2024). U.S. Monetary Policy Spillovers to Middle East and Central Asia: Shocks, Fundamentals, and Propagations. IMF Working Paper, 2024(014). Wang, D., Liu, Q., and Li, S. (2024). Analysing the Evolving Effectiveness of Monetary Policy Instruments in China: Insights from the TVPSV-MF-BFAVAR Framework. Applied Economics. Wieland, J. (2021). Compilation, Updated Romer-Romer Monetary Policy Shocks. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.
描述 碩士
國立政治大學
經濟學系
112258003
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112258003
資料類型 thesis
dc.contributor.advisor 林馨怡zh_TW
dc.contributor.advisor Lin, Hsin-Yien_US
dc.contributor.author (Authors) 李郁涵zh_TW
dc.contributor.author (Authors) Lee, Yu-Hanen_US
dc.creator (作者) 李郁涵zh_TW
dc.creator (作者) Lee, Yu-Hanen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Jul-2025 15:34:15 (UTC+8)-
dc.date.available 1-Jul-2025 15:34:15 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2025 15:34:15 (UTC+8)-
dc.identifier (Other Identifiers) G0112258003en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157858-
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) 112258003zh_TW
dc.description.abstract (摘要) 本文以MIDAS結合局部投影法，在有效運用不同頻率數據的資訊，並降低模型錯誤設置對估計結果的影響下，探討美國聯準會貨幣政策的國際外溢效果。以1988年第三季至2023年第三季的實證結果顯示，聯準會升息後，多數國家的實質產出成長率衝擊反應函數趨勢與美國相似，且受衝擊程度與美國相當，或甚至高於美國影響，此代表美國貨幣政策在全球景氣循環中具關鍵影響力，亦突顯關注外溢效果的必要性。而美國升息初期，產出上升之現象，可能與貨幣政策所蘊含的資訊效果有關。此外，本文比較有無混頻的估計結果，發現單一頻率模型的衝擊反應函數較平緩，顯示若未考慮混頻，將可能低估各國所受之擊。zh_TW
dc.description.tableofcontents 1 緒論 1 2 文獻回顧 4 2.1 貨幣政策的溢出與溢回效果 5 2.2 貨幣政策面臨的混合頻率問題 8 3 研究方法 12 3.1 混合頻率方法 12 3.2 混頻模型與衝擊反應函數 16 4 資料與模型 21 4.1 資料 21 4.2 模型設定 24 5 實證結果 28 5.1 基礎模型 28 5.2 不同貨幣政策衝擊變數 30 5.3 不同期間資料與權重 32 5.4 不同期數模型 34 5.5 bridge模型結果比較 34 6 結論 36 參考文獻 37 附錄 39zh_TW
dc.format.extent 2006219 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112258003en_US
dc.subject (關鍵詞) 混合頻率zh_TW
dc.subject (關鍵詞) 貨幣政策zh_TW
dc.subject (關鍵詞) 外溢效果zh_TW
dc.title (題名) 美國貨幣政策的國際外溢效果：結合局部投影法和混合頻率模型之應用zh_TW
dc.title (題名) International Spillover Effects of U.S. Monetary Policy Shocks: Combining the Local Projections and MIDAS Approachen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Bacchiocchi, E., Bastianin, A., Missale, A., and Rossi, E. (2016). Structural Analysis with Mixed Frequencies: Monetary Policy, Uncertainty and Gross Capital Flows. JRC Working Papers in Economics and Finance, 2016/4. Beirne, J., Renzhi, N., and Volz, U. (2023). When the United States and the People＇s Republic of China Sneeze: Monetary Policy Spillovers to Asian Economies. Open Economies Review, 34, 519–540. Bluwstein, K., and Canova, F. (2016). Beggar-Thy-Neighbor? The International Effects of ECB Unconventional Monetary Policy Measures. International Journal of Central Banking, 12(3), 69–120. Bonser-Neal, C., and Roley, V.V., and Sellon, G.H., Jr. (1998). Monetary Policy Actions, Intervention, and Exchange Rates: A Reexamination of the Empirical Relationships Using Federal Funds Rate Target Data. The Journal of Business, 71(2), 147–177. Breitenlechner, M., Georgiadis, G., and Schumann, B. (2022). What Goes Around Comes Around: How Large are Spillbacks from US Monetary Policy? Journal of Monetary Economics, 131, 45–60. Durdu, C.B., and Martin, A., and Zer, I. (2019). The Role of U.S. Monetary Policy in Global Banking Crises. Finance and Economics Discussion Series, 2019(39). Bu, C., and Rogers, J., and Wu, W. (2021). A unified measure of Fed monetary policy shocks. Journal of Monetary Economics, 118, 331–349. Federal Reserve Bank of Philadelphia (2025/1/30). Compilation, Tealbook (formerly Greenbook) Data Sets. Federal Reserve Bank of Philadelphia. Feldkircher, M., Huber, F., and Pfarrhofer, M. (2021). Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession. Scottish Journal of Political Economy, 68, 287–297. Ferrara, L., and Guérin, P. (2018). What are the Macroeconomic Effects of High-Frequency uncertainty shocks? Journal of Applied Econometrics, 33(5), 662–679. Foroni, C., and Marcellino, M. (2014). Mixed-Frequency Structural Models:Identification, Estimation, and Policy Analysis. Journal of Applied Econometrics, 29, 1118–1144. Francis, N., Ghysels , E., and Owyang, MT. (2011). The Low-Frequency Impact of Daily Monetary Policy Shocks. Federal Reserve Bank of St. Louis Working Paper, 2011(9). Georgiadis, G. (2016). Determinants of Global Spillovers from US Monetary Policy. Journal of International Money and Finance, 67, 41–61. Ghysels, E., Santa-Clara, P., and Valkanov, R. (2004). The MIDAS Touch: Mixed Data Sampling Regression Models. CIRANO Working Papers, 2004(20). Ghysels, E., Sinko, A., and Valkanov, R. (2007). MIDAS Regressions: Further Results and New Directions. Econometric Reviews, 26(1), 53–90. Ghysels, E., Kvedaras, V., and Zemlys, V. (2016). Mixed Frequency Data Sampling Regression Models: The R Package midasr. Journal of Statistical Software, 72(4), 1–35. Ghysels, E., Kvedaras, V., and Zemlys, V. (2020). Mixed Data Sampling (MIDAS) Regression Models. Handbook of Statistics, 42, 117–153. Jarociński, M., and Karadi, P. (2020). Deconstructing Monetary Policy Surprises—The Role of Information Shocks. American Economic Journal: Macroeconomics, 12(2), 1–43. Jordà, Ò. (2023). Local Projections for Applied Economics. Federal Reserve Bank of San Francisco Working Paper, 2023(16). Lastauskas, P., and Nguyen, A.D.M. (2023). Global Impacts of US Monetary Policy Uncertainty Shocks. Journal of International Economics, 145. Lin (2025). Real Time Data and Policy Evaluation. Working Paper. Marcellino, M., and Sivec, V. (2016). Monetary, Fiscal and Oil Shocks: Evidence based on Mixed Frequency Structural FAVARs. Journal of Econometrics, 193, 335–348. McCracken, M.W., Owyang , M., and Sekhposyan, T. (2015). Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR. Federal Reserve Bank of St. Louis Working Paper, 2015(30). Mohaddes, K., and Raissi, M. (2020). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2019Q4 University of Cambridge: Judge Business School (mimeo). Mohaddes, K., and Raissi, M. (2024). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2023Q3. University of Cambridge: Judge Business School (mimeo). Romer, C.D., and Romer, D.H. (2004). A New Measure of Monetary Shocks: Derivation and Implications. American Economic Review, 94(4), 1055–1084. Rooj, D., and Sengupta, R. (2018). Monetary Policy and Private Investment in India: The MIDAS Experience. Advances in Finance & Applied Economics, 119–131. Schumacher, C. (2016). A Comparison of MIDAS and Bridge Equations. International Journal of Forecasting, 32(2), 257–270. Ugazio, G., and Xin, W. (2024). U.S. Monetary Policy Spillovers to Middle East and Central Asia: Shocks, Fundamentals, and Propagations. IMF Working Paper, 2024(014). Wang, D., Liu, Q., and Li, S. (2024). Analysing the Evolving Effectiveness of Monetary Policy Instruments in China: Insights from the TVPSV-MF-BFAVAR Framework. Applied Economics. Wieland, J. (2021). Compilation, Updated Romer-Romer Monetary Policy Shocks. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.zh_TW