English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 118628/149684 (79%)
Visitors : 79927190      Online Users : 398
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/158274


    Title: 利用時變係數的分量因子模型分析菲利浦曲線
    An Empirical Analysis of the Phillips Curve via a Time-Varying Coefficient Quantile Factor Model
    Authors: 彭姿瑄
    Peng, Tzu-Hsuan
    Contributors: 徐士勛
    Hsu, Shih-Hsun
    彭姿瑄
    Peng, Tzu-Hsuan
    Keywords: 分量因子模型
    時變係數
    菲利浦曲線
    Quantile factor model
    Time-varying coefficients
    Phillips curve
    Date: 2025
    Issue Date: 2025-08-04 12:50:35 (UTC+8)
    Abstract: 本文使用 Atak et al. (2023) 提出隨時間變化的分量因子模型 (time-varying quantile factor models),探討 40 個國家於 1995 年第一季至 2023 年第四季菲利浦曲線的變化態勢。該模型在分量迴歸模型的框架中加入具時變因子負載的因子模型,使母體參數能夠隨分量、個體、時間不同而變化。我們在第一階段估計中先建立均數因子模型,利用局部主成分分析估計不可觀察的共同因子。第二階段則建立分量因子模型,將第一階段估計出的共同因子代入,並利用分量非參數估計法估計母體參數。透過上面兩個階段,我們可以估計不同分量下隨個體、時間變化的母體參數。實證分析中,我們參考 Kabundi et al. (2023) 討論通貨膨脹缺口與產出成長缺口關係的菲利浦曲線,並加入油價缺口變數作為全球供給衝擊的替代變數。
    大致而言,我們的實證結果發現各國在全球金融風暴後,菲利浦曲線斜率趨於平穩,直到新冠疫情時轉趨陡峭。我們也發現通貨膨脹缺口具有持續性,且不同分量下通貨膨脹缺口的持續性程度不同,通貨膨脹缺口越大持續性越高。此外,在控制石油價格缺口後,我們也發現通貨膨脹缺口的絕對值越大,持續性越高;相反,通貨膨脹缺口的絕對值越小,持續性越低。
    This study applies the time-varying quantile factor model by Atak et al. (2023) to examine the evolution of the Phillips curve in 40 countries from 1995Q1 to 2023Q4. The model integrates time-varying factor loadings into a quantile regression framework, allowing parameters to vary across quantiles, individuals, and time. In the two-stage estimation, we first extract unobservable common factors via local principal component analysis. In the second stage, we construct a quantile factor model by incorporating these estimated common factors and then estimate quantile-specific parameters using nonparametric methods. Following Kabundi et al. (2023), we analyze the relationship between the inflation gap and output growth gap, incorporating the oil price gap as a proxy for global supply shocks.
    Our empirical results show that the slope of the Phillips curve remained stable across countries after the Global Financial Crisis but steepened during the COVID-19 pandemic. The inflation gap exhibits varying degrees of persistence across quantiles, with greater persistence observed at larger gaps. After controlling for the oil price gap, we further find thatthe absolute value of the inflation gap is positively associated with its persistence.
    Reference: Ando, T. and Bai, J. (2015). Asset pricing with a general multifactor structure. Journal of Financial Econometrics, 13(3):556–604.
    Ando, T. and Bai, J. (2020). Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity. Journal of the American Statistical Association, 115(529):266–279.
    Atak, A., Montes-Rojas, G., and Olmo, J. (2023). Functional coefficient quantile regression model with time-varying loadings. Journal of Applied Economics, 26(1):2167151.
    Bai, J. (2003). Inferential theory for factor models of large dimensions. Econometrica, 71(1):135–171.
    Bai, J. (2009). Panel data models with interactive fixed effects. Econometrica, 77(4):1229–1279.
    Bai, J. and Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1):191–221.
    Bates, B. J., Plagborg-Møller, M., Stock, J. H., and Watson, M. W. (2013). Consistent factor estimation in dynamic factor models with structural instability. Journal of Econometrics, 177(2):289–304.
    Beaudry, P., Hou, C., and Portier, F. (2024). The Dominant Role of Expectations and BroadBased Supply Shocks in Driving Inflation. University of Chicago Press.
    Bobeica, E. and Jarociński, M. (2019). Missing disinflation and missing inflation: A var perspective. International Journal of Central Banking, 15(1):199–232.
    Cai, Z. and Xu, X. (2008). Nonparametric quantile estimations for dynamic smooth coefficient models. Journal of the American Statistical Association, 103(484):1595–1608.
    Chaudhuri, P., Doksum, K., and Samarov, A. (1997). On average derivative quantile regression. Annals of Statistics, 25(2):715–744.
    Chen, L., Dolado, J. J., and Gonzalo, J. (2021). Quantile factor models. Econometrica,
    89(2):875–910.
    De Gooijer, J. G. and Zerom, D. (2003). On conditional density estimation. Statistica
    Neerlandica, 57(2):159–176.
    Draghi, M. (2015). Structural reforms, inflation and monetary policy. Introductory speech at the ECB Forum on Central Banking, Sintra, 22 May 2015. https://www.ecb.europa.eu/press/key/date/2015/html/sp150522.en.html.
    Eichler, M., Motta, G., and von Sachs, R. (2011). Fitting dynamic factor models to nonstationary time series. Journal of Econometrics, 163(1):51–70.
    Florio, A., Siena, D., and Zago, R. (2025). Global value chains and the phillips curve: A challenge for monetary policy. European Economic Review, 174:104966.
    Friedman, M. (1968). The role of monetary policy. The American Economic Review, 58(1):1–17.
    Fu, B. (2020). Is the slope of the phillips curve time-varying? evidence from unobserved components models. Economic Modelling, 88:320–340.
    Galvao, A. F. and Montes-Rojas, G. (2015). On bootstrap inference for quantile regression panel data: A monte carlo study. Econometrics, 3(3):654–666.
    Hazell, J., Herreño, J., Nakamura, E., and Steinsson, J. (2022). The slope of the phillips curve: Evidence from u.s. states*. The Quarterly Journal of Economics, 137(3):1299– 1344.
    Hou, C., Fu, B., and Trinh, K. (2025). Estimating output gap with the time-varying slope new keynesian phillips curve. SSRN. Available at SSRN: https://ssrn.com/abstract=5115507 or http://dx.doi.org/10.2139/ssrn.5115507.
    Kabundi, A., Poon, A., and Wu, P. (2023). A time-varying phillips curve with global factors: Are global factors important? Economic Modelling, 126:106423.
    Kim, M.-O. (2007). Quantile regression with varying coefficients. The Annals of Statistics, 35(1):92 – 108.
    Koenker, R. and Bassett, G. (1978). Regression quantiles. Econometrica, 46(1):33–50.
    Koenker, R. and Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475):980–990.
    Lipsey, R. G. (1960). The relation between unemployment and the rate of change of money wage rates in the united kingdom, 1862-1957: A further analysis. Economica, 27(105):1– 31.
    Lucas, R. E. (1972). Expectations and the neutrality of money. Journal of Economic Theory, 4(2):103–124.
    Lucas, R. E. (1976). Econometric policy evaluation: A critique. Carnegie-Rochester Conference Series on Public Policy, 1:19–46.
    McLeay, M. and Tenreyro, S. (2020). Optimal inflation and the identification of the phillips curve. NBER Macroeconomics Annual, 34:199–255.
    McNeil, J. and Smith, G. W. (2023). The all-gap phillips curve. Oxford Bulletin of Economics and Statistics, 85(2):269–282.
    Moretti, L., Onorante, L., and Zakipour-Saber, S. (2019). Phillips curves in the euro area. Technical report, Central Bank of Ireland.
    Ng, M., Wessel, D., and Sheiner, L. (2018). The hutchins center explains: The phillips curve. Brookings Up Front (August 21).
    Phelps, E. S. (1967). Phillips curves, expectations of inflation and optimal unemployment over time. Economica, 34(135):254–281.
    Phillips, A. W. (1958). The relation between unemployment and the rate of change of money wage rates in the united kingdom, 1861–1957. Economica, 25(100):283–299.
    Samuelson, P. A. and Solow, R. M. (1960). Analytical aspects of anti-inflation policy. The American Economic Review, 50(2):177–194.
    Smith, S. C., Timmermann, A., and Wright, J. H. (2025). Breaks in the phillips curve: Evidence from panel data. Journal of Applied Econometrics, 40(2):131–148.
    Song, M. (2013). Essays on Large Panel Data Analysis. Ph.d. thesis, Columbia University.
    Stock, J. H. and Watson, M. W. (2020). Slack and cyclically sensitive inflation. Journal of Money, Credit and Banking, 52(S2):393–428. Su, L. and Wang, X. (2017). On time-varying factor models: Estimation and testing. Journal of Econometrics, 198(1):84–101.
    Wei, Y. and He, X. (2006). Conditional growth charts. The Annals of Statistics, 34(5):2069 – 2097.
    Description: 碩士
    國立政治大學
    經濟學系
    112258015
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112258015
    Data Type: thesis
    Appears in Collections:[經濟學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    801501.pdf774KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback