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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張宏浩(Hung-Hao Chang) | |
| dc.contributor.author | Ling-Yi Hung | en |
| dc.contributor.author | 洪綾憶 | zh_TW |
| dc.date.accessioned | 2021-05-20T20:18:45Z | - |
| dc.date.available | 2010-07-30 | |
| dc.date.available | 2021-05-20T20:18:45Z | - |
| dc.date.copyright | 2009-07-30 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-06-24 | |
| dc.identifier.citation | 中文部分
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Coglan, 2005. “Factors Affecting Technical Efficiency in Fsheries: Stochastic Production Frontier Versus Data Envelopment Analysis Approaches.” Fisheries Research 73 : 363–376. Van den Broeck, J., G. Koop, J. Osiewalski, and M.F.J. Steel, 1994. “Stochastic Frontier Models: A Bayesian Perspective.” Journal of Econometrics 61: 273–303. Wadud, A., and B. White, 2000.” Farm Household Efficiency in Bangladesh: A Comparison of Stochastic Frontier and DEA Methods .” Applied Economics 32: 1665–1673. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9352 | - |
| dc.description.abstract | 本研究應用三種不同方法，分別是：資料包絡法、傳統隨機邊界法以及貝氏隨機邊界法來計算各養殖魚種的生產技術效率。首先比較傳統與貝氏隨機邊界法估計的係數值，結果顯示生產函數的估計係數結果一致。至於生產技術效率的機率分配方面，本研究發現不論是哪種魚種，資料包絡法所估計的技術效率的機率分配都較兩種隨機邊界模型估計之技術效率的機率分配來得扁平且右偏。本文進一步利用Kruskal-Wallis檢定法來比較三種不同方法所估計的生產技術效率值的機率分配，結果顯示不同方式所得到的技術效率之分配在統計上是不同的。而各養殖漁戶的排序方面，本研究應用Spearman等級檢定，發現貝氏與傳統的隨機邊界模型都是顯著地高度相關、而資料包絡法與其他兩種隨機邊界法的相關程度則是：石斑魚、文蛤、牡蠣以及鱸魚為顯著高度相關、剩下的魚種為顯著地低度相關。
最後比較不同年間的生產技術效率變化，結果顯示不論是用哪種估計方法，1999年至2000年間有較大的差異，之後有起有落，但差距都不大。若僅使用貝氏的隨機邊界模型比較2000年以及2006年的技術效率差異，則可以發現除了在低技術效率的部分以外，2006年的技術效率在各百分位數都較2000年高；又2006年技術效率值的集中處在0.85以後，但是反觀2000年的技術效率集中處則是在0.5以及0.75之處，這就是造成兩年技術效率差異的原因。 | zh_TW |
| dc.description.abstract | The objective of this paper is threefold. First, this paper applies three different methodologies, including Data Envelope Analysis (DEA), classical stochastic frontier analysis and Bayesian stochastic frontier analysis, to estimate technical efficiency of aquaculture farms in Taiwan. In what follows, we compare these three technical efficiencies estimated by these three different methodologies. We conduct the analysis in several steps. We first compare the estimated coefficient of the production function between Bayesian and stochastic production frontier models. In addition, we compare these distributions of technical efficiency by utilizing two different non-parametric methods. To test whether these distributions were the same, the Kruskal-Wallis test is conducted. To further test whether the relative rank of aquaculture farms were consistent between the estimation methods, we use the Spearman Rank test. The final objective of this study is to investigate if the technical efficiency has been changed between 1999 and 2006. First we compare the distribution of technical efficiency obtained from three different methodologies from 1999 to 2006. Finally, we use the results obtained from Bayesian stochastic frontier model to compare the distribution of technical efficiency between 2000 and 2006.
Empirical results show that the coefficients of two stochastic frontier models are almost the same. But when compare the production elasticity, the conclusion is that not only the sigh but the most important input exist slightly differences between two methodologies. In addition, the distribution of technical efficiency estimated by DEA is right-skewed, and flatter than the distribution of technical efficiency estimated by other two methods regardless of the fish types. With respect to the examination if the distributions of technical efficiency were the same, results show that at least two distributions are statistically different. In terms of the relative rank of these aquaculture farms, Bayesian and classical stochastic frontier analysis are statistically correlated, but DEA and two stochastic frontier methodologies are strongly correlated only in some kinds of fish and the others are weakly correlated. This study analyses the change of technical efficiency across years, and results show that regardless of these different methodologies, there’s a significant differences between 1999 and 2000. After 2000 there’re some up and down in these years, but the scale is minor. Focusing on the change of technical efficiency between 2000 and 2006, results show that when it compares all percentiles, 2006 are higher than 2000. Most importantly, technical efficiencies are centralized around 0.85 in 2006, but centralized around 0.5 and 0.75 in 2000, that’s the reason why technical efficiencies are higher in 2006. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T20:18:45Z (GMT). No. of bitstreams: 1 ntu-98-R96627013-1.pdf: 798904 bytes, checksum: baf634aaf28bf86b1f55b4ad33e4307d (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | 中文摘要………………………………….…..………………………………i
英文摘要………………………………………………………………………………...ii 第一章 緒論…………………………………………………………………………..1 1.1研究動機與研究目的……………………………………………………...….1 1.2研究方法及步驟………………………………………………………………4 1.3論文架構……………………………………………………...……………….5 第二章 臺灣養殖漁業現況…………………………………………………………..6 2. 1臺灣養殖漁業重要性………………………………………..……………….6 2.2臺灣的養殖漁業現況…………………………………………...…………...13 第三章 文獻回顧……………………………………………………………………19 3.1養殖漁業技術效率分析回顧……………………………………..…………19 3.2估計技術效率方法之回顧……...…………………………………………...30 3.2.1貝氏隨機邊界模型文獻之回顧……………...………………………30 3.2.2資料包絡法和傳統隨機邊界模型的比較…………………………...30 3.2.3三種估計技術效率的方法之回顧…………………………………31 第四章 理論模型…………………………………………………………………...33 4.1資料包絡法……………………….…………………...……………………..33 4.2傳統隨機邊界模型………………………………………...……………...…37 4.3貝氏隨機邊界模型………………...……………………………………...…39 4.3.1貝氏定理……………………………………..……………………….40 4.3.1.1事前機率密度函數……………………………………………40 4.3.1.2概似函數………………………………………………………40 4.3.1.3事後機率密度函數………………………………………….41 4.3.1.4馬可夫鍊蒙特卡羅…………………………………………..41 4.3.2貝氏隨機邊界模型…………………………………………………...43 4.4 比較方法…………………………………………………………………………..45 4.4.1 Kruskal-Wallis檢定………………………………………………………45 4.4.2 Spearman等級檢定……………………………………………………….46 第五章 資料收集與介紹……………………………………...……….…………....48 5.1資料收集………………………….………………………..…..…………….48 5.2估計技術效率投入項…………………..……………………………………50 第六章 實證分析……………………………………………………………………54 6.1係數估計結果……………………………………………………………...54 6.2技術效率值之比較………………………………………………………...58 6.3年間比較………………………………………………...…………………...66 6.3.1三種方法之比較………………………………………………...……66 6.3.2使用貝氏隨機邊界模型比較前後兩年技術效率……………………70 第七章 結論與建議…………………………………………………………………73 7.1 結論…………………………………………………………...……………..73 7.2未來發展以及研究限制………………………………………..……………74 參考文獻……………………………………………….…………………………...…76 附錄一：各魚種貝式以及傳統隨機邊界模型結果…………………………………..84 附錄二：各魚種三種不同方法下之技術效率機率密度函數圖……………………..91 附錄三：不同年間三種技術效率的機率密度函數圖…………..……………………95 圖 目 錄 圖2-1：1998年至2007年漁業產值百分比……………………………………………10 圖2-2：1998年至2007年漁戶百分比…………………………………………………13 圖2-3：2007年養殖面積百分比………………………………………………………15 圖2-4：2007 年各魚種養殖產量百分比……………………………………………16 圖2-5：2007 年各魚種養殖產值百分比……………………………………………16 圖4-1：投入取向………………………………………………………………………34 圖4-2：產出取向………………………………………………………………………35 圖4-3：固定規模報酬與變動規模報酬………………………………………………36 圖4-4：隨機邊界模型…………………………………………………………………37 圖6-1：吳郭魚勞動投入係數項隨著更新次數變化的情形…………………………55 圖6-2：吳郭魚β3的機率密度函數圖………………………………………………55 圖6-3：吳郭魚β1的機率密度函數圖………………………………………………56 圖6-4：不同魚種三種不同估計方法的平均值………………………………………60 圖6-5：吳郭魚三種不同方法下之技術效率機率密度函數圖………………………62 圖6-6：2006 年三種不同方法技術效率的機率密度函數圖………………………66 圖6-7：1999 年至 2006 年間技術效率平均值的變化……………………………70 圖6-8：2000 年以及 2006 年貝氏隨機邊界模型技術效率機率密度函數圖……72 附圖2-1：虱目魚三種不同方法下之技術效率機率密度函數圖……………………92 附圖2-2：石斑魚三種不同方法下之技術效率機率密度函數圖……………………92 附圖2-3：文蛤三種不同方法下之技術效率機率密度函數圖………………………93 附圖2-4：牡蠣三種不同方法下之技術效率機率密度函數圖………………………93 附圖2-5：鱸魚三種不同方法下之技術效率機率密度函數圖………………………94 附圖3-1：1999年三種技術效率的機率密度函數圖………………………………96 附圖3-2：2000年三種技術效率的機率密度函數圖………………………………96 附圖3-3：2001年三種技術效率的機率密度函數圖………………………………97 附圖3-4：2002年三種技術效率的機率密度函數圖………………………………97 附圖3-5：2003年三種技術效率的機率密度函數圖………………………………98 附圖3-6：2004年三種技術效率的機率密度函數圖………………………………98 附圖3-7：2005年三種技術效率的機率密度函數圖………………………………….99 表 目 錄 表1-1：2002 年至 2006 年全世界漁業產量以及消費量……………………………1 表2-1：1998 年至 2007 年農業部門產值與百分比…………………………………7 表2-2：1998年至2007 年漁業產值…………………………………………………9 表2-3：1998 年至2007年漁業產量及其百分比……………………………………11 表2-4：1998年至2007 年漁戶數……………………………………………………12 表3-1：國外文獻有關養殖漁業技術效率的整理……………………………………21 表3-2：國內文獻有關養殖漁業技術效率的整理……………………………………27 表5-1：1999年至2006 年魚種以及樣本數…………………………………………49 表5-2：各魚種投入之比重……………………………………………………………50 表5-3：各魚種使用變數之基本統計量………………………………………………52 表6-1：吳郭魚隨機邊界模型之係數估計結果比較…………………………………57 表6-2：各魚種應用三種不同方法估計之技術效率比較……………………………58 表6-3：不同方法不同魚種之Spearman檢定結果…………………………………64 表6-4：不同年間不同方法所估計的技術效率比較…………………………………68 表6-5：2000年與 2006 年貝氏技術效率比較……………………………………71 附表1-1：鰻魚隨機邊界模型之係數估計結果………………………………………85 附表1-2：虱目魚隨機邊界模型之係數估計結果……………………………………86 附表1-3：石斑魚隨機邊界模型之係數估計結果……………………………………87 附表1-4：文蛤隨機邊界模型之係數估計結果………………………………………88 附表1-5：鱸魚隨機邊界模型之係數估計結果………………………………………89 附表1-6：牡蠣隨機邊界模型之係數估計結果………………………………………90 | |
| dc.language.iso | zh-TW | |
| dc.title | 探討歷年台灣養殖漁業生產技術效率之變遷 | zh_TW |
| dc.title | Investigating the Changes of the Technical Efficiencies of the Aquaculture Production in Taiwan | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 蕭清仁(Chiang-Ren Show) | |
| dc.contributor.oralexamcommittee | 傅祖壇,劉擎華 | |
| dc.subject.keyword | 技術效率,養殖漁業,資料包絡法,隨機邊界模型,貝氏定理, | zh_TW |
| dc.subject.keyword | technical efficiency,aquaculture,Data envelope analysis,Stochastic frontier analysis, | en |
| dc.relation.page | 99 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2009-06-24 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農業經濟學研究所 | zh_TW |
| 顯示於系所單位： | 農業經濟學系 | |
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