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題名 使用NSPF並考慮負利潤樣本下檢定我國銀行業代理成本假說
Testing the agency cost hypothesis in Taiwan`s banking industry using NSPF and considering negative profit samples
作者 鄭人瑋
Jeng, Ren-Wei
貢獻者 黃台心<br>江彌修
Huang, Tai-Hsin<br>Chiang, Mi-Hsiu
鄭人瑋
Jeng, Ren-Wei
關鍵詞 非標準利潤函數
利潤效率
代理成本
規模經濟
Non-standard profit function
Profit efficiency
Agency cost
Increasing returns to scale
日期 2023
上傳時間 1-Sep-2023 14:46:51 (UTC+8)
摘要 本文運用Restrepo-Tobón and Kumbhakar (2017) 提出的非標準利潤函數，並將負利潤樣本值納入分析估計效率值，並探討代理成本問題，首先在虛擬變數模型與刪除模型不支持代理成本，縮放模型此結果支持代理成本，NPI模型結果無法判定支持代理成本假說與否。在分群效率結果，顯示縮放模型因為虛增每個樣本點的利潤值，易導致高估利潤效率；刪除法完全排除負利潤樣本，則傾向低估利潤效率；虛擬變數法與NPI法的平均效率值比較接近。三類銀行比較方面，縮放法發現私營銀行平均效率值最高，公營銀行次之，外商銀行最低外，其他三個模型都指向公營銀行的平均效率值最高，私營銀行次之，外商銀行最低。金控與非金控方面除縮放模型支持非金控銀行的平均效率值優於金控銀行外，其餘三模型皆支持金控銀行效率優於非金控銀行。成本函數分群結果，在跨期方面，效率值排序為金融數位轉型2019-2021最佳，全球金融危機後的調整期間2010-2018次之，最低為金融自由化期間2002-2009;三個期間皆有符合規模經濟與範疇經濟。三類銀行比較方面，效率值排序為外商銀行的平均效率最高，私營銀行次之，公營銀行最低;規模經濟方面，三類銀行的平均規模經濟值差異不大，皆小於1，顯示三類銀行皆處於報酬遞增生產階段;範疇經濟方面，三類銀行的平均範疇經濟估計值皆大於1，尤以公營估計較大。金控與非金控方面，金控效率值低於非金控效率值;在規模經方面，兩者平均規模經濟值差異不大，皆小於1，顯示金控與非金控皆有規模經濟; 範疇經濟方面，金控與非金控平均範疇經濟估計值皆大於1，尤以金控較大。
This text employs the non-standard profit function proposed by Restrepo-Tobón and Kumbhakar (2017) and incorporates negative profit sample values into the efficiency estimation analysis, exploring the agency cost issue. Firstly, in the virtual variable model and the deletion model, support for agency costs is not present. However, the scaling model supports the agency cost hypothesis. The results from the NPI model do not allow a determination of support or rejection for the agency cost hypothesis.In terms of efficiency results for different groups, the scaling model tends to overestimate profit efficiency due to inflating the profit values of each sample point. The deletion method, which completely excludes negative profit samples, leans towards underestimating profit efficiency. The virtual variable and NPI methods yield average efficiency values that are relatively close.
Comparing three types of banks, the scaling method reveals that private banks have the highest average efficiency, followed by public banks, and foreign banks have the lowest efficiency. However, the other three models point towards public banks having the highest average efficiency, followed by private banks, and foreign banks having the lowest. In terms of financial holding companies and non-financial holding companies, except for the scaling model supporting higher average efficiency for non-financial holding banks over financial holding banks, the remaining three models all support higher efficiency for financial holding banks over non-financial holding banks.In the context of cost function grouping results, across time periods, efficiency value rankings are as follows: the best efficiency is observed during the financial digital transformation period of 2019-2021, followed by the adjustment period after the global financial crisis (2010-2018), and the lowest efficiency is during the financial liberalization period of 2002-2009. All three periods exhibit compliance with economies of scale and scope.
Comparing the three types of banks, efficiency value rankings are as follows: foreign banks exhibit the highest average efficiency, followed by private banks, and public banks have the lowest efficiency. In terms of economies of scale, the average values for the three types of banks show minor differences and are all less than 1, indicating that all three types of banks are in the stage of increasing returns to scale. Regarding economies of scope, the average estimated values for economies of scope for all three types of banks are greater than 1, with public banks showing the highest estimate.
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描述 博士
國立政治大學
金融學系
102352503
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102352503
資料類型 thesis
dc.contributor.advisor 黃台心<br>江彌修zh_TW
dc.contributor.advisor Huang, Tai-Hsin<br>Chiang, Mi-Hsiuen_US
dc.contributor.author (Authors) 鄭人瑋zh_TW
dc.contributor.author (Authors) Jeng, Ren-Weien_US
dc.creator (作者) 鄭人瑋zh_TW
dc.creator (作者) Jeng, Ren-Weien_US
dc.date (日期) 2023en_US
dc.date.accessioned 1-Sep-2023 14:46:51 (UTC+8)-
dc.date.available 1-Sep-2023 14:46:51 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2023 14:46:51 (UTC+8)-
dc.identifier (Other Identifiers) G0102352503en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146857-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 102352503zh_TW
dc.description.abstract (摘要) 本文運用Restrepo-Tobón and Kumbhakar (2017) 提出的非標準利潤函數，並將負利潤樣本值納入分析估計效率值，並探討代理成本問題，首先在虛擬變數模型與刪除模型不支持代理成本，縮放模型此結果支持代理成本，NPI模型結果無法判定支持代理成本假說與否。在分群效率結果，顯示縮放模型因為虛增每個樣本點的利潤值，易導致高估利潤效率；刪除法完全排除負利潤樣本，則傾向低估利潤效率；虛擬變數法與NPI法的平均效率值比較接近。三類銀行比較方面，縮放法發現私營銀行平均效率值最高，公營銀行次之，外商銀行最低外，其他三個模型都指向公營銀行的平均效率值最高，私營銀行次之，外商銀行最低。金控與非金控方面除縮放模型支持非金控銀行的平均效率值優於金控銀行外，其餘三模型皆支持金控銀行效率優於非金控銀行。成本函數分群結果，在跨期方面，效率值排序為金融數位轉型2019-2021最佳，全球金融危機後的調整期間2010-2018次之，最低為金融自由化期間2002-2009;三個期間皆有符合規模經濟與範疇經濟。三類銀行比較方面，效率值排序為外商銀行的平均效率最高，私營銀行次之，公營銀行最低;規模經濟方面，三類銀行的平均規模經濟值差異不大，皆小於1，顯示三類銀行皆處於報酬遞增生產階段;範疇經濟方面，三類銀行的平均範疇經濟估計值皆大於1，尤以公營估計較大。金控與非金控方面，金控效率值低於非金控效率值;在規模經方面，兩者平均規模經濟值差異不大，皆小於1，顯示金控與非金控皆有規模經濟; 範疇經濟方面，金控與非金控平均範疇經濟估計值皆大於1，尤以金控較大。zh_TW
dc.description.abstract (摘要) This text employs the non-standard profit function proposed by Restrepo-Tobón and Kumbhakar (2017) and incorporates negative profit sample values into the efficiency estimation analysis, exploring the agency cost issue. Firstly, in the virtual variable model and the deletion model, support for agency costs is not present. However, the scaling model supports the agency cost hypothesis. The results from the NPI model do not allow a determination of support or rejection for the agency cost hypothesis.In terms of efficiency results for different groups, the scaling model tends to overestimate profit efficiency due to inflating the profit values of each sample point. The deletion method, which completely excludes negative profit samples, leans towards underestimating profit efficiency. The virtual variable and NPI methods yield average efficiency values that are relatively close.
Comparing three types of banks, the scaling method reveals that private banks have the highest average efficiency, followed by public banks, and foreign banks have the lowest efficiency. However, the other three models point towards public banks having the highest average efficiency, followed by private banks, and foreign banks having the lowest. In terms of financial holding companies and non-financial holding companies, except for the scaling model supporting higher average efficiency for non-financial holding banks over financial holding banks, the remaining three models all support higher efficiency for financial holding banks over non-financial holding banks.In the context of cost function grouping results, across time periods, efficiency value rankings are as follows: the best efficiency is observed during the financial digital transformation period of 2019-2021, followed by the adjustment period after the global financial crisis (2010-2018), and the lowest efficiency is during the financial liberalization period of 2002-2009. All three periods exhibit compliance with economies of scale and scope.
Comparing the three types of banks, efficiency value rankings are as follows: foreign banks exhibit the highest average efficiency, followed by private banks, and public banks have the lowest efficiency. In terms of economies of scale, the average values for the three types of banks show minor differences and are all less than 1, indicating that all three types of banks are in the stage of increasing returns to scale. Regarding economies of scope, the average estimated values for economies of scope for all three types of banks are greater than 1, with public banks showing the highest estimate.		en_US
dc.description.tableofcontents 第一章 緖論.......................................1
第一節 研究背景與動機........................1
第二節 研究目的..............................2
第三節 研究架構與流程........................3
第二章 文獻回顧...................................5
第一節 標準與非標準利潤函數..................5
第二節 處理負利潤方法相關文獻................7
第三節 成本函數與規模經濟...................10
第四節 代理理論相關文獻.....................10
第三章 研究方法..................................12
第一節 非標準利潤函數模型...................12
第二節 處理負利潤方法與環境變數模型.........12
第四章 資料分析..................................16
第一節 各變數敘述統計量.....................16
第二節 環境變數說明.........................17
第五章 實證結果..................................23
第一節 係數估計.............................23
第二節 分群效率比較.........................25
第三節 範疇經濟與規模經濟分析...............28
第四節 結論.................................29
參考文獻.........................................45
附錄.............................................54		zh_TW
dc.format.extent 1655514 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102352503en_US
dc.subject (關鍵詞) 非標準利潤函數zh_TW
dc.subject (關鍵詞) 利潤效率zh_TW
dc.subject (關鍵詞) 代理成本zh_TW
dc.subject (關鍵詞) 規模經濟zh_TW
dc.subject (關鍵詞) Non-standard profit functionen_US
dc.subject (關鍵詞) Profit efficiencyen_US
dc.subject (關鍵詞) Agency costen_US
dc.subject (關鍵詞) Increasing returns to scaleen_US
dc.title (題名) 使用NSPF並考慮負利潤樣本下檢定我國銀行業代理成本假說zh_TW
dc.title (題名) Testing the agency cost hypothesis in Taiwan`s banking industry using NSPF and considering negative profit samplesen_US
dc.type (資料類型) thesisen_US
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