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題名 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
作者 彭姿瑄
Peng, Tzu-Hsuan
貢獻者 徐士勛
Hsu, Shih-Hsun
彭姿瑄
Peng, Tzu-Hsuan
關鍵詞 分量因子模型
時變係數
菲利浦曲線
Quantile factor model
Time-varying coefficients
Phillips curve
日期 2025
上傳時間 4-Aug-2025 12:50:35 (UTC+8)
摘要 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.
參考文獻 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.
描述 碩士
國立政治大學
經濟學系
112258015
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112258015
資料類型 thesis
dc.contributor.advisor 徐士勛zh_TW
dc.contributor.advisor Hsu, Shih-Hsunen_US
dc.contributor.author (Authors) 彭姿瑄zh_TW
dc.contributor.author (Authors) Peng, Tzu-Hsuanen_US
dc.creator (作者) 彭姿瑄zh_TW
dc.creator (作者) Peng, Tzu-Hsuanen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 12:50:35 (UTC+8)-
dc.date.available 4-Aug-2025 12:50:35 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 12:50:35 (UTC+8)-
dc.identifier (Other Identifiers) G0112258015en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158274-
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) 112258015zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 1 緒論 4 2 文獻回顧 6 2.1 時變係數的分量因子模型 6 2.2 菲利浦曲線 8 3 模型建構與估計 10 3.1 模型建構 10 3.2 第一階段估計：均數因子模型 11 3.3 第二階段估計：時變分量迴歸模型 14 4 實證分析 16 4.1 資料 16 4.2 時變係數的分量因子模型 19 5 結論與討論 28 參考文獻 30 附錄 33 A 所有國家敘述統計 33 B 延伸估計結果 35zh_TW
dc.format.extent 792903 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112258015en_US
dc.subject (關鍵詞) 分量因子模型zh_TW
dc.subject (關鍵詞) 時變係數zh_TW
dc.subject (關鍵詞) 菲利浦曲線zh_TW
dc.subject (關鍵詞) Quantile factor modelen_US
dc.subject (關鍵詞) Time-varying coefficientsen_US
dc.subject (關鍵詞) Phillips curveen_US
dc.title (題名) 利用時變係數的分量因子模型分析菲利浦曲線zh_TW
dc.title (題名) An Empirical Analysis of the Phillips Curve via a Time-Varying Coefficient Quantile Factor Modelen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 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.zh_TW