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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/124212


    Title: 各經濟體在金融危機下的異質性時間軌跡探討
    Discussion of grouped patterns of heterogeneity in the financial crisis
    Authors: 陳又華
    Chen, You-Hua
    Contributors: 徐士勛
    陳又華
    Chen, You-Hua
    Keywords: 金融危機
    分群
    固定效果
    時間效果
    K-means
    VNS
    Date: 2019
    Issue Date: 2019-07-01 11:02:51 (UTC+8)
    Abstract: 本篇論文應用 Bonhomme and Manresa (2015) 提出的分群方法，依各
    國在金融危機期間呈現的經濟態勢進行分群，此方法在 k-means 概念下搭配變動鄰域搜尋法以降低起始值對分群結果的影響。我們以不良貸款占總貸款比例、私營機構放貸比率、通膨率及淨借出/借入占 GDP 比率等四項自變數與經濟成長變化率建構主要的分群迴歸式，並將分群結果經由變數和時間效果走勢凸顯出各群異質性程度。藉由此研究，我們可以將分群結果與文獻做比較，並依據異質性程度提出更完整及豐富的分析。
    我們的實證結果顯示，2011年之後經濟復甦的走勢是各群異質性的主
    要來源，我們進而將各群分為首波復甦經濟體、次波復甦經濟體及不穩定
    經濟體三類，私營機構放貸比率則是決定金融危機期間經濟成長變化率關
    鍵的變數。此外，我們發現開放式的經濟體、未加入歐元區的歐盟國家、
    受到金融危機影響較小的拉丁美洲及亞洲國家及曾在金融危機期間接受歐
    盟及國際組織紓困的經濟體在分群結果上較不受到利用單一時點的資料與
    一段期間的連續資料分群的結果影響；並根據文獻與本文研究的結果推論
    出亞洲金融風暴後危機應變能力提升可能強化了亞洲各國金融危機後的復
    甦力道及紓困計畫可能對受助國產生雙管齊下的效果。至於各群在金融危
    機導引期及發生期間變數走勢的差異以私營機構放貸比率最為顯著。
    Reference: Afonso, A. M. . K. A., A. (2014). Pricing sovereign bond risk in the european monetary union area: an empirical investigation. International Journal of Finance and Economics, 19(1), 49–56. doi: 10.1002/ijfe
    Argüelles, M., Benavides, C., & Fernández, I. (2014). A new approach to the identification of regional clusters. Applied Economics, 46(21), 2511–2519.
    Bien, J., & Tibshirani, R. (2011). Hierarchical clustering with prototypes via minimax linkage. Journal of the American Statistical Association, 106(495), 1075–1084. doi: 10.1198/jasa.2011.tm10183
    Bonhomme, S., & Manresa, E. (2015). Grouped patterns of heterogeneity in panel data. Econometrica, 83(3), 1147–1184. doi: 10.3982/ecta11319
    Brownstein, M. J., Khodursky, A., Ben-Hur, A., & Guyon, I. (2003). Detecting stable clusters using principal component analysis. Functional Genomics(1), 159–182. doi: 10.1385/1-59259-364-x:159
    Chen, R. B., Chen, Y. C., Chu, C. H., & Lee, K. J. (2017). On the determinants of the 2008 financial crisis: a bayesian approach to the selection of groups and variables. Studies in Nonlinear Dynamics and Econometrics, 21(5), 1–17. doi: 10.1515/snde-2016-0107
    CIMPOERU, S. (2015). Models for evaluating financial crisis –what went wrong for developed and emerging economies? Theoretical and Applied Economics, XXII, 273-282.
    Hansen, P., Mladenovic, N., & Moreno, J. (2010). Variable Neighbourhood Search. Inteligencia Artificial, 7(19), 1097–1100. doi: 10.4114/ia.v7i19.717
    Reutter, A. (2003). Twostep cluster analysis. IBM Software, Business Analytics, SPSS, 1–5. Retrieved from http://www1.uni-hamburg.de/ RRZ/Software/SPSS/Algorith.120/twostep_cluster.pdf doi:
    23/1/303 [pii]
    Rose, A. K., & Spiegel, M. M. (2012). Cross-country causes and consequences of the 2008 crisis: early warning. Japan and the World Economy, 24(1), 1–16. Retrieved from http:// dx.doi.org/ 10.1016/ j.japwor. 2011.11.001 doi: 10.1016/j.japwor.2011.11.001
    Syed Zwick, H., & Syed, S. A. S. (2017). Great recession impact on european labor markets integration: cluster analyses. International Journal of Manpower, 38(7), 1016–1035. doi: 10.1108/IJM-03-2016-0059
    Description: 碩士
    國立政治大學
    經濟學系
    106258014
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106258014
    Data Type: thesis
    DOI: 10.6814/NCCU201900047
    Appears in Collections:[Department of Economics] Theses

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