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  • 数据包络分析中的生产规模研究(英文) 杨国梁 编 专业科技 文轩网
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    • 作者: 暂无著
    • 出版社: 知识产权出版社
    • 出版时间:2020-12-01 00:00:00
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    • 作者: 暂无著
    • 出版社:知识产权出版社
    • 出版时间:2020-12-01 00:00:00
    • 版次:1
    • 印次:1
    • 印刷时间:2020-12-01
    • 字数:600000
    • 页数:376
    • 开本:16开
    • 装帧:平装
    • ISBN:9787513073165
    • 国别/地区:中国
    • 版权提供:知识产权出版社

    数据包络分析中的生产规模研究(英文)

    作  者:杨国梁 编
    定  价:188
    出 版 社:知识产权出版社
    出版日期:2020年12月01日
    页  数:376
    装  帧:平装
    ISBN:9787513073165
    主编推荐

    内容简介

    DEA(data envelopment analysis)方法及其模型主要应用于投入产出效率评价、全要素生产率分析、生产函数构建以及组织标杆设定等领域。本书主要汇编了DEA框架中与生产规模相关的三个重要经济学概念[规模收益(RTS)、阻塞(Congestion)和产能利用率(CU)]相关的理论研究方法及相应的实践应用和思考。本书主要供经济学、运筹学和统计学相关研究人员、宏观管理人员以及相关领域研究生阅读使用。

    作者简介

    精彩内容

    目录
    Chapter 1 Estimating Directional Returns to Scale in DEA
    1.1 Introduction
    1.2 Classical RTS in DEA framework
    1.3 Directional SE and directional RTS
    1.4 Measurement of directional RTS
    1.5 A case study
    1.6 Conclusions
    Chapter 2 Data Envelopment Analysis in the Absence of Convexity: Specifying Efficiency Status and Estimating Returns to Scale
    2.1 Introduction
    2.2 Preliminaries and literature review
    2.3 Methodology
    2.4 Most productive scale size
    2.5 Illustrative examples
    2.6 Conclusions and future extensions
    Chapter 3 Institutional Change and Optimal Size of Universities
    3.1 Introduction
    3.2 Background and theory
    3.3 Data and identification strategy
    3.4 Results
    3.5 Discussions and conclusions
    Chapter 4 A Study on Directional Returns to Scale
    4.1 Introduction
    4.2 Methodology
    4.3 Analysis of directional RTS and directional congestion effect
    4.4 Conclusions and discussions
    Chapter 5 Directional Congestion in the Framework of Data Envelopment Analysis
    5.1 Introduction
    5.2 Primary approaches to congestion measurement
    5.3 Definitions of directional congestion
    5.4 Measurement of directional congestion
    5.5 A case study
    5.6 Conclusions
    Chapter 6 Integer Data in DEA: Illustrating the Drawbacks and Recognizing Congestion
    6.1 Introduction
    6.2 Classical congestion
    6.3 Karimi et al.'s (2016) congestion approach
    6.4 The drawbacks of the PEIC
    6.5 Recognizing congestion with both negative and/or non-negative continuous and integer data
    6.6 Graphical illustration of our proposed approach
    6.7 Numerical example
    6.8 Empirical application
    6.9 Concluding remarks and possible extensions
    Chapter 7 Negative Data in DEA: Recognizing Congestion and Specifying the Least and the Most Congested Decision-Making Units
    7.1 Introduction
    7.2 Implications of congestion and negative data in DEA
    7.3 The proposed congestion approach
    7.4 Specifying the strongly and weakly most congested DMUs in the presence of negative data
    7.5 Ranking of the congested DMUs in the presence of negative data
    7.6 Numerical example
    7.7 Empirical application
    7.8 Conclusions and future extensions
    Chapter 8 Estimating Capacity Utilization of Chinese Manufacturing Industries
    8.1 Introduction
    8.2 Literature review
    8.3 Methodology and indicators
    8.4 Empirical results
    8.5 Conclusions and discussions
    Chapter 9 Measuring the Chinese Regional Production Potential Using A Generalized Capacity Utilization Indicator
    9.1 Introduction
    9.2 Literature review
    9.3 Generalized capacity utilization indicator
    9.4 Empirical study:Chinese regions
    9.5 Conclusions and discussions
    Chapter 10 Estimating Capacity Utilization of Chinese State Farms
    10.1 Introduction
    10.2 Literature review
    10.3 Methodology
    10.4 Results and policy implication
    10.5 Conclusions
    Chapter 11 Measuring the Capacity Utilization of the 48 Largest Iron and Steel Enterprises in China
    11.1 Introduction
    11.2 Literature review
    11.3 Notation and models
    11.4 Dataset and input and output variables
    11.5 Empirical results
    11.6 Conclusions and discussions

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