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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王泓仁(Hung-Jen Wang),陳南光(Nan-Kuang Chen) | |
| dc.contributor.author | Pang-Yu Wang | en |
| dc.contributor.author | 王邦瑜 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:34:03Z | - |
| dc.date.available | 2020-08-08 | |
| dc.date.copyright | 2017-08-08 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-02 | |
| dc.identifier.citation | 1. Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of econometrics, 6(1), 21–37.
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On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of econometrics, 19(2-3), 233–238. 19. Kinukawa, S., & Motohashi, K. (2010). Bargaining in technology markets: An empirical study of biotechnology alliances. Discussion papers, 10020. 20. Kumbhakar, S. C. (1990). Production frontiers, panel data, and time-varying technical inefficiency. Journal of econometrics, 46(1-2), 201–211. 21. Kumbhakar, S. C., & Heshmati, A. (1995). Efficiency measurement in swedish dairy farms: an application of rotating panel data, 1976–88. American Journal of Agri- cultural Economics, 77(3), 660–674. 22. Kumbhakar, S. C., Lien, G., & Hardaker, J. B. (2014). Technical efficiency in competing panel data models: a study of norwegian grain farming. Journal of Productivity Analysis, 41(2), 321–337. 23. Kumbhakar, S. C., & Parmeter, C. F. (2009). 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Journal of econometrics, 13(1), 57–66. 36. Tomini, S., Groot, W., & Pavlova, M. (2012). Paying informally in the albanian health care sector: a two-tiered stochastic frontier model. The European Journal of Health Economics, 13(6), 777–788. 37. Tsionas, E. G., & Kumbhakar, S. C. (2014). Firm heterogeneity, persistent and transient technical inefficiency: A generalized true random-effects model. Journal of Applied Econometrics, 29(1), 110–132. 38. Wang, H.-J., & Ho, C.-W. (2010). Estimating fixed-effect panel stochastic frontier models by model transformation. Journal of Econometrics, 157(2), 286–296. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67480 | - |
| dc.description.abstract | 本文在以下兩方面拓展了 Polacheck and Yoon (1996) 的架構，並建構一個六成分追蹤隨機邊界模型 (以下簡稱本模型)：一、本模型多包含了具隨機效果 (random effect) 的個人異質性 (individual heterogeneity)，二、本模型揭示如何將雙層架構下不隨時間變動的不效率效果 (time-invariant inefficiency effects) 判別 (identify) 出來。Polacheck and Yoon (1996) 雖有包括不隨時間變動的不效率效果，但在估計上卻無法判別出來。
我們採用 Kumbhakar et al. (2014) 的兩階段估計法來估計模型。該法的優點在於易於估計，而且不需要六個隨機變數的卷積 (convoluted) 機率密度函數的封閉解 (closed form)。對於不效率項 (inefficiency terms)，我們考慮三種分配假設：截斷常態分佈 (truncated-normal distribution)、半常態分佈 (half-normal distribution)，與指數分佈 (exponential distribution)。當分配假設為截斷常態分佈時，本文在文獻中是第一篇推導出關鍵的機率密度函數，並在第二階段的估計中使用；我們也推導了 Jondrow et al. (1982) 的不效率指標 (inefficiency index) 與 Battese and Coelli (1988) 的效率指標 (efficiency index)。 在本模型的架構下，許多其他的隨機邊界模型是本模型的特例。這代表本模型在文獻上可以被看作是較為一般化的情形。 最後，在假設所有的不效率項服從指數分配的形下，我們提供了蒙地卡羅模擬以觀察本模型估計值的誤差與均方差 (mean squared error)。 | zh_TW |
| dc.description.abstract | In this thesis, we propose a six-component panel stochastic frontier (SF) model that extends the two-tier panel SF model of Polacheck and Yoon (1996) in two major ways: (1) It includes individual heterogeneity which is treated as a random effect. (2) It shows how the time-invariant inefficiency effects in both of the tiers can be identified. The model of Polacheck and Yoon (1996) includes the time-invariant inefficiency effects, but they are not identified in the estimation.
We use the two-step estimation approach of Kumbhakar et al. (2014) to estimate the model. The approach is easy to conduct and does not require a closed form of the convoluted density of all the model's six random components. We consider three distributional assumptions on the inefficiency terms: truncated-normal, half-normal, and exponential. For the case of the truncated-normal, we are the first in the literature to derive key density functions which are used in the second step of the estimation; we also derive the (in)efficiency indexes of Jondrow et al. (1982) and Battese and Coelli (1988) for such a model. Our model nests many of the existing SF models as special cases. This indicates that our model is one of the more general models in the literature. Finally, we present Monte Carlo simulations to observe biases and mean squared errors of the estimates in our model with two-tier normal-exponential specifications. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:34:03Z (GMT). No. of bitstreams: 1 ntu-106-R04323037-1.pdf: 1568311 bytes, checksum: 51b1ff8e06e0830739b8ebf4edcfdf13 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | Contents
1 Introduction 4 2 Literature Review 8 2.1 One-Tier SF Models 8 2.2 Two-Tier SF Models 9 3 The Model 13 3.1 Step 1: Random-Effect GLS Estimation 14 3.2 Step 2: ML Estimation 14 3.3 Distributional Assumption 1 17 3.3.1 Time-Invariant Components 17 3.3.2 Time-Varying Components 20 3.4 Distributional Assumption 2 24 3.4.1 Time-Invariant Components 24 3.4.2 Time-Varying Components 26 3.5 Distributional Assumption 3 28 3.5.1 Time-Invariant Components 28 3.5.2 Time-Varying Components 29 4 Comparison 31 5 Monte Carlo Simulations 36 5.1 Discussion 1: Increase N only 56 5.2 Discussion 2: Increase T only 66 5.3 Summary 71 6 Conclusion and Future Research 72 References 74 Appendix A Derivations in Distributional Assumption 1 78 A.1 Density of z = −u + w 78 A.2 Density of ε˜ = v − u + w = v + z 82 A.3 Nest the One-Tier SF Models 89 A.4 Conditional Densities 91 A.5 Inefficiency Estimates of Jondrow et al. (1982) 93 A.6 Efficiency Estimates of Battese and Coelli (1988) 95 List of Figures 1 Fix T = 5 58 2 Fix T = 5, More Detailed 59 3 Fix T = 15 60 4 Fix T = 15, More Detailed 61 5 Fix T = 50 62 6 Fix T = 50, More Detailed 63 7 Fix T = 100 64 8 Fix T = 100, More Detailed 65 9 Fix N = 200 67 10 Fix N = 300 68 11 Fix N = 400 69 12 Fix N = 1000 70 List of Tables 1 Distributional Assumptions 16 2 Cross-Sectional Model Comparison (T = 1) 33 3 Panel Model Comparison 34 4 Biases 39 5 MSEs 39 6 Simulation Results with N = 200, T = 5 40 7 Simulation Results with N = 200, T = 15 41 8 Simulation Results with N = 200, T = 50 42 9 Simulation Results with N = 200, T = 100 43 10 Simulation Results with N = 300, T = 5 44 11 Simulation Results with N = 300, T = 15 45 12 Simulation Results with N = 300, T = 50 46 13 Simulation Results with N = 300, T = 100 47 14 Simulation Results with N = 400, T = 5 48 15 Simulation Results with N = 400, T = 15 49 16 Simulation Results with N = 400, T = 50 50 17 Simulation Results with N = 400, T = 100 51 18 Simulation Results with N = 1000, T = 5 52 19 Simulation Results with N = 1000, T = 15 53 20 Simulation Results with N = 1000, T = 50 54 21 Simulation Results with N = 1000, T = 100 55 22 Terms and Estimates 56 | |
| dc.language.iso | en | |
| dc.subject | 雙層隨機邊界模型 | zh_TW |
| dc.subject | 截斷常態分佈 | zh_TW |
| dc.subject | 最大概似估計法 | zh_TW |
| dc.subject | 隨機效果 | zh_TW |
| dc.subject | 一般最小平方估計法 | zh_TW |
| dc.subject | 追蹤資料 | zh_TW |
| dc.subject | Maximum Likelihood Estimation | en |
| dc.subject | Two-Tier Stochastic Frontier Model | en |
| dc.subject | Truncated-Normal Distribution | en |
| dc.subject | Random Effect | en |
| dc.subject | Generalized Least Square (GLS) Estimation | en |
| dc.subject | Panel Data | en |
| dc.title | 六成分追蹤隨機邊界模型 | zh_TW |
| dc.title | Six-Component Panel Stochastic Frontier Model | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張勝凱,賴宏彬 | |
| dc.subject.keyword | 雙層隨機邊界模型,截斷常態分佈,最大概似估計法,隨機效果,一般最小平方估計法,追蹤資料, | zh_TW |
| dc.subject.keyword | Two-Tier Stochastic Frontier Model,Truncated-Normal Distribution,Maximum Likelihood Estimation,Random Effect,Generalized Least Square (GLS) Estimation,Panel Data, | en |
| dc.relation.page | 103 | |
| dc.identifier.doi | 10.6342/NTU201702234 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-02 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 經濟學研究所 | zh_TW |
| 顯示於系所單位： | 經濟學系 | |
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