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本人学习DEA期间，软件和网站情况如下：

1. 一、deap2.1
   
2. http://www.uq.edu.au/economics/cepa/software.php
   
3. 二、matlab的deatoolbox
   
4. https://bbs.pinggu.org/thread-4975049-1-1.html
   
5. 这个需要先安装matlab并将这个工具箱加载到matlab工作路径中方可调用
   
6. 三、OSDEA
   
7. http://opensourcedea.org/?title=Open_Source_DEA
   
8. 这个是开源的DEA软件，下载了OSDEA GUI之后，按照网站上的说明将
   
9. swt.jar file粘到lib文件夹之中就可启用[/size][/font][/b]

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1. 四、R语言的Benchmarking的package https://bbs.pinggu.org/thread-3571386-1-1.html[/indent]

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以上是本人找到资料，希望对大家有帮助！
也欢迎大家进行补充，以供坛友进行学习！
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关键词：学习心得 DEA Economics 希望对大家有帮助 software

1. DEAP V2.1
   
2. 
   
3. A Data Envelopment Analysis (Computer) Program
   
4. 
   
5. This page describes the computer program DEAP Version 2.1 which was written by Tim Coelli. This program is used to construct DEA frontiers for the calculation of technical and cost efficiencies and also for the calculation of Malmquist TFP Indices.
   
6. 
   
7. The program has three principle DEA options:
   
8. Standard CRS and VRS DEA models that involve the calculation of technical and scale efficiencies (where applicable).
   
9. 
   
10. The extension of the above models to account for cost and allocative efficiencies.
    
11. 
    
12. The application of Malmquist DEA methods to panel data to calculate indices of total factor productivity (TFP) change; technological change; technical efficiency change and scale efficiency change.
    
13. 
    
14. All methods are available in either an input or an output orientation (with the exception of the cost efficiencies option). The output from the program includes, where applicable, technical, scale, allocative and cost efficiency estimates; slacks; peers; and TFP indices.
    
15. 
    
16. The program is compiled using a Lahey F77LEM/32 compiler for an IBM compatible PC. The program package includes the executable program; data files for four simple examples; and a 47 page user’s guide (in pdf format).
    
17. 
    
18. If you wish to obtain a copy of the program you can download the zip file DEAP-xp1.zip which contains all the necessary files.
    
19. 
    
20. If you obtain a copy of the program you are advised to send some brief email to Tim Coelli at t.coelli@economics.uq.edu.au so that he can advise you of any bugs or new versions.
    
21. 
    
22. If you have any technical questions regarding the use of this computer program you can contact Tim Coelli by email at t.coelli@economics.uq.edu.au. Brief questions (involving less than 10 minutes of time) will be answered as quickly as is possible. A consultancy service is available for those who have more lengthy enquiries. Consultancy fees will be provided on request.

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OSDEA

1. Why Open Source DEA?
   
2. 
   
3. There exist DEA software programs although these are generally either not really comprehensive or really expensive. Besides, few simple open source DEA projects can be found. This project is an attempt to provide free (as in free beer but also freedom to use and modify) open source code which can be used and modified by anyone.
   
4. 
   
5. Who could be interested?
   
6. 
   
7. This project is of use to people who have an interest in DEA and who have some knowledge of programming in JAVA. This could particularly be:
   
8. 
   
9. Researchers,
   
10. Students,
    
11. Operational Research folks.
    
12. Anybody with an interest in Data Envelopment Analysis open source code and software.
    
13. Contribution
    
14. 
    
15. The following people have contributed either directly or through the Creative Common License ©:
    
16. 
    
17. Hubert Virtos, the project holder.
    
18. Cihan Cetin, Ian Cliffe and Richard Harrop for their help and suggestion on how to improve the code.
    
19. Expert web monkey Ryan Bolton who spent hours explaining me the basics of HTML, css and scripting languages.
    
20. Sirea icons from rw-designer.com.
    
21. Interested in contributing? If you are interested in the project and want to contribute to developing OSDEA, just send an email so that you can get started!
    
22. 
    
23. Why the cooking pot icon?
    
24. 
    
25. No particular reason except that it looks nice.
    
26. 
    
27. Icon was found in an icon library made by Sirea.
    
28. 
    
29. Contact
    
30. 
    
31. Please do not hesitate to send an email should you have any question or suggestions! Any feedback is always welcomed!

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matlab的DEA toolbox

1. DEA Toolbox
   
2. 
   
3. Data Envelopment Analysis Toolbox is a new package for MATLAB that includes functions to calculate efficiency and productivity measures. The package covers the radially oriented, directional, additive, allocative, Malmquist índices and Malmquist-Luenberger formulations.

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谢谢分享~

1. Benchmarking-package {Benchmarking}        R Documentation
   
2. Data Envelopment Analyses (DEA) and Stochastic Frontier Analyses (SFA) – Model Estimations and Efficiency Measuring
   
3. 
   
4. Description
   
5. 
   
6. The Benchmarking package contains methods to estimate technologies and measure efficiencies using DEA and SFA. Data Envelopment Analysis (DEA) are supported under different technology assumptions (fdh, vrs, drs, crs, irs, add), and using different efficiency measures (input based, output based, hyperbolic graph, additive, super, directional). Peers are available, partial price information can be included, and optimal cost, revenue and profit can be calculated. Evaluation of mergers are also supported. A comparative method for estimating stochastic frontier function (SFA) efficiencies is included. The methods can solve not only standard models, but also many other model variants, and they can be modified to solve new models.
   
7. 
   
8. The package also support simple plots of DEA technologies with two goods; either as a transformation curve (2 outputs), an isoquant (2 inputs), or a production function (1 input and 1 output). When more inputs and outputs are available they are aggregated using weights (prices, relative prices).
   
9. 
   
10. The package complements the book, Bogetoft and Otto, Benchmarking with DEA, SFA, and R, Springer-Verlag 2011, but can of course also be used as a stand-alone package.
    
11. 
    
12. Details
    
13. 
    
14. Package:         Benchmarking
    
15. Type:         Package
    
16. Version:         0.26 ($Revision: 152 $)
    
17. Date:         $Date: 2015-07-07 12:02:02 +0200 (ti, 07 jul 2015) $
    
18. License:         Copyright
    
19. dea         DEA input or output efficience measures, peers, lambdas and slacks
    
20. dea.dual         Dual weights (prices), including restrictions on weights
    
21. dea.direct         Directional efficiency
    
22. sdea         Super efficiency.
    
23. dea.add         Additive efficiency; sum of slacks in DEA technology.
    
24. mea         Multidirectional efficiency analysis or potential improvements.
    
25. eff         Efficiency from an object returned from any of the dea or sfa functions.
    
26. slack         Slacks in DEA models
    
27. excess         Calculates excess input or output compared to DEA frontier.
    
28. peers         get the peers for each firm.
    
29. dea.boot         Bootstrap DEA models
    
30. cost.opt         Optimal input for given output and prices.
    
31. revenue.opt         Optimal output for given input and prices.
    
32. profit.opt         Optimal input and output for given input and output prices.
    
33. dea.plot         Graphs of DEA technologies under alternative technology assumptions.
    
34. dea.plot.frontier         Specialized for 1 input and 1 output.
    
35. dea.plot.isoquant         Specialized for 2 inputs.
    
36. dea.plot.transform         Specialized for 2 outputs.
    
37. eladder         Efficiency ladder for a single firm.
    
38. eladder.plot         Plot efficiency ladder for a single firm.
    
39. make.merge         Make an aggregation matrix to perform mergers.
    
40. dea.merge         Decompose efficiency from a merger of firms
    
41. sfa         Stochastic frontier analysis, production, distance, and cost function (SFA)
    
42. outlier.ap         Detection of outliers
    
43. eff.dens         Estimate and plot kernel density of efficiencies
    
44. critValue         Critical values calculated from bootstrap DEA models.
    
45. typeIerror         Probability of a type I error for a test in bootstrap DEA models.
    
46. Note
    
47. 
    
48. The interface for the methods are very much like the interface to the methods in the package FEAR (Wilson 2008). One change is that the data now are transposed to reflect how data is usually available in applications, i.e. we have firms on rows, and inputs and output in the columns. Also, the argument for the options RTS and ORIENTATION can be given as memotechnical strings, and there are more options to control output.
    
49. 
    
50. The input and output matrices can contain negative numbers, and the methods can thereby manage restricted or fixed input or output.
    
51. 
    
52. The return is not just the efficiency, but also slacks, dual values (shadow prices), peers, and lambdas (weights).
    
53. 
    
54. Author(s)
    
55. 
    
56. Peter Bogetoft and Lars Otto larsot23@gmail.com
    
57. 
    
58. References
    
59. 
    
60. Bogetoft and Otto; Benchmarking with DEA, SFA, and R; Springer 2011
    
61. 
    
62. Paul W. Wilson (2008), “FEAR 1.0: A Software Package for Frontier Efficiency Analysis with R,” Socio-Economic Planning Sciences 42, 247–254
    
63. 
    
64. Examples
    
65. 
    
66. # Plot of different technologies
    
67. x <- matrix(c(100,200,300,500),ncol=1,dimnames=list(LETTERS[1:4],"x"))
    
68. y <- matrix(c(75,100,300,400),ncol=1,dimnames=list(LETTERS[1:4],"y"))
    
69. dea.plot(x,y,RTS="vrs",ORIENTATION="in-out",txt=rownames(x))
    
70. dea.plot(x,y,RTS="drs",ORIENTATION="in-out",add=TRUE,lty="dashed",lwd=2)
    
71. dea.plot(x,y,RTS="crs",ORIENTATION="in-out",add=TRUE,lty="dotted")
    
72.                      
    
73. dea.plot(x,y,RTS="fdh",ORIENTATION="in-out",txt=rownames(x),main="fdh")
    
74. dea.plot(x,y,RTS="irs",ORIENTATION="in-out",txt=TRUE,main="irs")
    
75. dea.plot(x,y,RTS="irs2",ORIENTATION="in-out",txt=rownames(x),main="irs2")
    
76. dea.plot(x,y,RTS="add",ORIENTATION="in-out",txt=rownames(x),main="add")
    
77. 
    
78. #  A quick frontier with 1 input and 1 output
    
79. dea.plot(x,y, main="Basic plot of frontier")
    
80. 
    
81. # Calculating efficiency
    
82. dea(x,y, RTS="vrs", ORIENTATION="in")
    
83. e <- dea(x,y, RTS="vrs", ORIENTATION="in")
    
84. e
    
85. eff(e)
    
86. peers(e)
    
87. peers(e, NAMES=TRUE)
    
88. print(peers(e, NAMES=TRUE), quote=FALSE)
    
89. lambda(e)
    
90. summary(e)
    
91. 
    
92. 
    
93. # Calculating super efficiency
    
94. esuper <- sdea(x,y, RTS="vrs", ORIENTATION="in")
    
95. esuper
    
96. print(peers(esuper,NAMES=TRUE),quote=FALSE)
    
97. # Technology for super efficiency for firm number 3/C
    
98. # Note that drop=FALSE is necessary for XREF and YREF to be matrices
    
99. # when one of the dimensions is or is reduced to 1.
    
100. e3 <- dea(x,y, XREF=x[-3,,drop=FALSE], YREF=y[-3,,drop=FALSE])
     
101. dea.plot(x[-3],y[-3],RTS="vrs",ORIENTATION="in-out",txt=LETTERS[c(1,2,4)])
     
102. points(x[3],y[3],cex=2)
     
103. text(x[3],y[3],LETTERS[3],adj=c(-.75,.75))
     
104. e3 <- dea(x,y, XREF=x[-3,,drop=FALSE], YREF=y[-3,,drop=FALSE])
     
105. eff(e3)
     
106. peers(e3)
     
107. print(peers(e3,NAMES=TRUE),quote=FALSE)
     
108. lambda(e3)
     
109. e3$lambda
     
110. 
     
111. # Taking care of slacks
     
112. x <- matrix(c(100,200,300,500,100,600),ncol=1,
     
113.         dimnames=list(LETTERS[1:6],"x"))
     
114. y <- matrix(c(75,100,300,400,50,400),ncol=1,
     
115.         dimnames=list(LETTERS[1:6],"y"))
     
116. 
     
117. # Phase one, calculate efficiency
     
118. e <- dea(x,y)
     
119. print(e)
     
120. peers(e)
     
121. lambda(e)
     
122. # Phase two, calculate slacks (maximize sum of slacks)
     
123. sl <- slack(x,y,e)
     
124. data.frame(sl$sx,sl$sy)
     
125. peers(sl)
     
126. lambda(sl)
     
127. sl$lambda
     
128. summary(sl)
     
129. 
     
130. # The two phases in one function call
     
131. e2 <- dea(x,y,SLACK=TRUE)
     
132. print(e2)
     
133. data.frame(eff(e2),e2$slack,e2$sx,e2$sy,lambda(e2))
     
134. peers(e2)
     
135. lambda(e2)
     
136. e2$lambda

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谢谢大神们分享，可否帮忙回答我一个问题？为什么综合效率=技术效率*规模效率，而不是技术效率+规模效率？

Rita666 发表于 2018-3-19 16:00
谢谢大神们分享，可否帮忙回答我一个问题？为什么综合效率=技术效率*规模效率，而不是技术效率+规模效率？

看看这个文档。这是下载的别人的，忘了在哪下载的了，如果有涉及侵权之类的，请联系删除！

拂去尘缘 发表于 2018-3-19 22:22
看看这个文档。这是下载的别人的，忘了在哪下载的了，如果有涉及侵权之类的，请联系删除！

好哒，非常感谢！

感谢，非常感谢