MaxDEA 12.5下载（免安装+长期有效）

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MaxDEA 12.5下载（免安装+长期有效）
MaxDEA 12.5下载（免安装+长期有效）
MaxDEA 12.5下载（免安装+长期有效）


下载地址：点击这里


MaxDEA12.5有很多新功能，支持下列模型：

Model Type (模型分类)

    1)Envelopment Model (包络模型)

    2)Multiplier Model (乘数模型)

    3)FDH 模型

Distance to measure efficiency (用于测量效率的距离类型)

    1) Radial (CCR 1978;BCC 1984) (径向距离)

    2) Maximum Distanceto Frontier (ERM, Enhanced Russel Measure, Pastor, Ruiz, and Sirvent 1999; SBM,Slacks-based Measure, Tone 2001) (至强有效前沿的最远距离，即SBM模型)

    3) Minimum Distanceto Weak Efficient Frontier (Charnes, Roussea, and Semple1996) (至弱有效前沿的最近距离)

    4) Minimum Distanceto Strong Efficient Frontier (Closest Target), with full features: strongmonotonicity algorithm; CRS, VRS, NIRS and NDRS; Non-oriented, Input-orientedand Output-oriented. (至强有效前沿的最近距离，MinDS距离)

        Ref.to  (J. Aparicio etal., 2007; G. R. Jahanshahloo et al., 2012; Gholam Reza Jahanshahloo, Roshdi,& Davtalab-Olyaie, 2013; Olyaie, Roshdi, Jahanshahloo, & Asgharian,2014; J Aparicio et al., 2017; Zhu et al., 2018; Zhu et al., 2022)

    5) DirectionalDistance Function (Chambers, Chung, and Färe 1996; Chung, Färe, and Grosskopf1997) (方向距离函数)

Direction Vector can be (方向向量类型)

        a): ( -|x0|, |y0|, -|b0|)'

        b): ( -|x̅|, |y̅|, -|b̅| )'

        c) Vector (1, 1, ..., 1)'

        d): Range (RDM, Portela, Thanassoulis, and Simpson 2004)

        e) Customized (same for all DMUs) (为所有DMU自定义相同的方向向量)

        f) Customized (DMU specific) (为各DMU自定义不同的方向向量)

    6)A Series ofWeighted Additive Models (加权加性距离)

        a) Simple Additivemodel: Weights = (1, 1, 1, ...) (不加权)

        b) NormalizedWeighted Additive (Lovell and Pastor 1995) (标准化权重)

        c) Weights = 1/|x0|,1/|y0|

        d) Weights =1/|x̅|,  1/|y̅|

        e) Range AdjustedMeasure (RAM, Cooper, Park, and Pastor 1999) (RAM模型)

        f) BoundedAdjusted Measure (BAM, Cooper, Pastor, Borras, Aparicio, and Pastor 2011) (BAM模型)

        g) DirectionalSlacks-based Measure (DSBM, Fukuyama and Weber 2009) (定向SBM模型)

        h) CustomizedWeights (same for all DMUs) (为各DMU自定义相同的权重)

        i) CustomizedWeights (DMU specific) (为各DMU自定义不同的权重，使用此类型实现一般化的“非径向方向距离函数”，即NDDF模型)

    7)HybridDistance(Radial and SBM Measure): (EBM, Epsilon-based Meaure,Tone and Tsutsui2010) (EBM模型)

    8)Cost (成本效率)

    9)Revenue (收益效率)

Orientation to measure efficiency (模型导向)

    1) Input-oriented (投入导向)

    2) Output-oriented (投入导向)

    3) Non-oriented (非导向)

RTS to measure efficiency (规模报酬)

    1) Constant returns to scale (CRS) (规模报酬不变)

    2) Variable returns to scale (VRS) (规模报酬可变)

    3) Non-increasing returns to scale (NIRS)(规模报酬非增)

    4) Non-decreasing returns to scale (NDRS)(规模报酬非减)

    5) Decomposition of Efficiency or TFPIndex (效率或生产率指数分解)

TFP Index: Malmquist Index andHicks-Moorsteen Index (also called HMB Index)

(全要素生产率指数: Malmquist指数和HMB指数)

    a) Adjacent Malmquist (相邻参比)

    b) Fixed Malmquist (固定参比)

    c) Global Malmquist (全局参比)

    d) Sequential Malmquist (序列参比)

    e) Window-Malmquist (Adjacent) (相邻窗口参比)

    f) Window-Malmquist (Fixed) (固定窗口参比)

    g) Global with Sequential Malmquist (全局和序列参比)

    TFP index decomposition: Efficiency Change (catch-up),Technological Change (frontier shift), Scale Efficiency Change, biased TechnologicalChange, TC=OBTC*IBTC*MATC (Fare et al 1997)

    Three types of indices can be computed: Index(t-1, t);Index (t-n, t) : n is user-defined; Index (t0,t): t0 is theinitial period.

Window DEA (窗口DEA)

Cluster model (群组参比模型，广义DEA模型)

    a) Self-benchmarking (自我参比)

    b) Cross-benchmarking (交叉参比)

    c) Downward-benchmarking (向下参比)

    d) Upward-benchmarking (向上参比)

    e) Lower-adjacent-benchmarking (下方临群参比)

    f) Upper-adjacent-benchmarking (上方临群参比)

    g) Window-benchmarking (窗口参比)

    h) Fixed-benchmarking (固定参比)

Other models

    1) Super-efficiency model (超效率模型)

    2) Modified SBM (Sharp et al 2007) (MSBM模型)

    3) Modified SBM (Lin et al 2019) (MSBM模型)

    4) Cross efficiency model (交叉效率模型)

        Second-stage methods are available (第2阶段方法):

        Minimize/Maximize the trade balance of other DMUs as awhole

            a) Blanket Benevolent (Type I in Doyle and Green 1995)

            b) Blanket Aggressive (Type I in Doyle and Green 1995)

        Maximize/Minimize the cross-efficiency of other DMUs as awhole

            c) Blanket Benevolent (Type II in Doyle and Green 1995)

            d) Blanket Aggressive (Type II in Doyle and Green 1995)

        Maximize/Minimizethe cross-efficiency of other DMUs as a whole

            e) Blanket Benevolent (Ruiz (2013))

            f) BlanketAggressive (Ruiz (2013))

        Maximize/Minimizethe cross-efficiency of a customized virtual DMU

            g) Benevolent (customized)

            h) Aggressive(customized)

    5) Game Cross Efficiency model: NashEquilibrium model (Liang, et al 2008; Wu, et al 2009) (博弈交叉效率模型)

    6) Undesirable outputs, desirable inputs (非期望产出(越少越好的产出) 和 期望投入(越多越好的投入))

    7) Nondiscretionary input/output model (不可随意控制的投入产出)

    8) Preference (weighted) model (SetInput/output Weights) (偏好(加权)模型，设置投入产出权重)

    9) Restricted multiplier model (assuranceregion model, trade-offs between inputs and outputs) (权重比值约束模型)

全新版还有以下特性：

◉    使用简单，原生跨平台支持Windows、macOS 和 Linux

◉    极速求解大数据DEA模型

◉    全面支持MinDS模型最新进展：强单调性算法; CRS、VRS、NIRS和NDRS; 非导向、投入导向和产出导向

◉    可输出所有FDEF(Full Dimensional Efficient Facets)：FDEF的顶点和超平面方程（分段前沿生产函数）



注：
1.无木马，无广告，纯干货，全套包含“程序+示例数据+操作指引+教程”
2.长期可用，不用担心任何有效期的问题，所有功能无限期用
3.运行速度非常快，包含大量数据的前沿模型也可以快速跑出来
4.完整版不限数据量