gstslshet(sphet)
gstslshet()所属R语言包：sphet
GM estimation of a Cliff-Ord type model with Heteroskedastic Innovations
GM估计型与异方差创新模式悬崖条例
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Multi step GM/IV estimation of a linear Cliff and Ord -type of model of the form:
多步GM / IV估计的线性的悬崖和条例型模型的形式：
with
同
The model allows for spatial lag in the dependent variable and disturbances. The innovations in the disturbance process are assumed heteroskedastic of an unknown form.
该模型允许空间滞后因变量和干扰。扰动过程中的创新是一个未知形式的异方差的假设。
用法----------Usage----------
gstslshet(formula, data=list(),listw,na.action=na.fail,zero.policy=NULL,initial.value=0.2,abs.tol=1e-20,rel.tol=1e-10,eps=1e-5,inverse=TRUE,sarar=TRUE)
## S3 method for class 'gstsls'
impacts(obj, ..., tr, R = NULL, listw = NULL, tol = 1e-06, empirical = FALSE, Q=NULL)
参数----------Arguments----------
参数：formula
a description of the model to be fit
描述的模型来适应
参数：data
an object of class data.frame. An optional data frame containing the variables in the model.
对象类数据框。一个可选的数据框包含在模型中的变量。
参数：listw
an object of class listw created for example by nb2listw
类的一个对象listw的nb2listw例如创建
参数：na.action
a function which indicates what should happen when the data contains missing values. See lm for details.
一个函数，它表示数据中包含缺失值的时会发生什么。请参阅流明的详细信息。
参数：zero.policy
See lagsarlm for details
lagsarlm的详细信息，
参数：initial.value
The initial value for ρ. It can be either numeric (default is 0.2) or set to 'SAR', in which case the optimization will start from the estimated coefficient of a regression of the 2SLS residuals over their spatial lag (i.e. a spatial AR model)
初始值ρ。它可以是数值（默认为0.2）或'SAR'，在这种情况下，优化会从他们的空间滞后（即空间AR模型的估计系数的2SLS残差的回归）
参数：abs.tol
Absolute tolerance. See nlminb for details.
绝对公差。有关详细信息，请参见nlminb。
参数：rel.tol
Relative tolerance. See nlminb for details.
相对宽容。有关详细信息，请参见nlminb。
参数：eps
Tolerance level for the approximation. See Details.
公差等级的近似。查看详细信息。
参数：inverse
TRUE. If FALSE, an appoximated inverse is calculated. See Details.
TRUE。如果FALSE，一个appoximated的逆的计算方法。查看详细信息。
参数：sarar
TRUE. If FALSE, a spatial error model is estimated.
TRUE。如果FALSE，空间误差模型估计。
参数：obj
A gstsls spatial regression object created by gstslshet
一个gstsls空间回归对象创建的gstslshet
参数：...
Arguments passed through to methods in the coda package
传递参数的方法，在coda包
参数：tr
A vector of traces of powers of the spatial weights matrix created using trW, for approximate impact measures; if not given, listw must be given for exact measures (for small to moderate spatial weights matrices); the traces must be for the same spatial weights as were used in fitting the spatial regression
使用的矢量权力的空间权重矩阵的痕迹trW，近似影响的措施;如果没有给出，listw必须给出确切的措施（小到中雨空间权重矩阵）;走线必须是相同的空间权重被用于安装的空间回归
参数：R
If given, simulations are used to compute distributions for the impact measures, returned as mcmc objects
如果给出，模拟用于计算分布的影响的措施，作为mcmc对象返回
参数：tol
Argument passed to mvrnorm: tolerance (relative to largest variance) for numerical lack of positive-definiteness in the coefficient covariance matrix
参数传递给mvrnorm：宽容（相对最大变化）的数值缺乏的系数协方差矩阵的正定性，
参数：empirical
Argument passed to mvrnorm (default FALSE): if true, the coefficients and their covariance matrix specify the empirical not population mean and covariance matrix
参数传递给mvrnorm（默认为false）：如果为真，系数和协方差矩阵指定的经验而不是人口的均值和协方差矩阵
参数：Q
default NULL, else an integer number of cumulative power series impacts to calculate if tr is given
默认为空，否则一个整数的累计幂级数的影响，计算，如果tr
Details
详细信息----------Details----------
The procedure consists of two steps alternating GM and IV estimators. Each step consists of sub-steps. In step one δ = [β',λ]' is estimated by 2SLS. The 2SLS residuals are first employed to obtain an initial (consistent but not efficient) GM estimator of ρ and then a consistent and efficient estimator (involving the variance-covariance matrix of the limiting distribution of the normalized sample moments). In step two, the spatial Cochrane-Orcutt transformed model is estimated by 2SLS. This corresponds to a GS2SLS procedure. The GS2SLS residuals are used to obtain a consistent and efficient GM estimator for ρ.
该过程包括两个步骤交替GM和IV估计。每个步骤包括子步骤。在步骤1 δ = [β',λ]'估计2SLS。 2SLS残差是第一次使用，获得一致的，但效率不高）初始（GM估计ρ，然后统一而有效的估计（涉及的归一化样本矩的极限分布的方差 - 协方差矩阵）。在步骤2中，该空间的科克伦 - 奥克特变换模型的估计2SLS。这对应到GS2SLS程序。 GS2SLS残差获得一致和有效的GM估计ρ。
The initial value for the optimization in step 1b is taken to be initial.value. The initial value in step 1c is the optimal parameter of step 1b. Finally, the initial value for the optimization of step 2b is the optimal parameter of step 1c.
步骤1b中的优化的初始值是initial.value。在步骤1c中的初始值是步骤1b中的最佳参数。最后，步骤2b中的优化的初始值是步骤1c中的最佳参数。
Internally, the object of class listw is transformed into a Matrix using the function listw2dgCMatrix.
在内部，对象的类listw一个矩阵的功能listw2dgCMatrix的转化为。
The expression of the estimated variance covariance matrix of the limiting distribution of the normalized sample moments based on 2SLS residuals involves the inversion of I-ρ W'. When inverse is FALSE, the inverse is calculated using the approximation I +ρ W' + ρ^2 W'^2 + ...+ ρ^n W'^n. The powers considered depend on a condition. The function will keep adding terms until the absolute value of the sum of all elements of the matrix ρ^i W^i is greater than a fixed ε (eps). By default eps
的极限分布的归一化的样本矩根据2SLS残差估计的方差协方差矩阵中的表达涉及反转I-ρ W'。当inverse是FALSE，逆计算近似I +ρ W' + ρ^2 W'^2 + ...+ ρ^n W'^n。考虑赋予的权力依赖的条件。该功能将不断增加的sum的所有元素的矩阵ρ^i W^i，直到绝对值是大于固定的ε（eps）。默认情况下，eps
值----------Value----------
A list object of class sphet
一个List对象的类sphet
参数：coefficients
Generalized Spatial two stage least squares coefficient estimates of δ and GM estimator for ρ.
广义空间两阶段最小二乘估计系数为δρ和GM估计的。
参数：var
variance-covariance matrix of the estimated coefficients
方差 - 协方差矩阵的估计系数
参数：s2
GS2SLS residuals variance
GS2SLS残差方差
参数：residuals
GS2SLS residuals
GS2SLS残差
参数：yhat
difference between GS2SLS residuals and response variable
GS2SLS残差和响应变量之间的差异
参数：call
the call used to create this object
用于创建此对象的调用
参数：model
the model matrix of data
的数据的模型矩阵
参数：method
'gs2slshac'
'gs2slshac'
参数：W
Wald test for both ρ and λ are zero
Wald检验为ρ和λ是零
（作者）----------Author(s)----------
Gianfranco Piras <a href="mailto:gpiras@mac.com">gpiras@mac.com</a>
参考文献----------References----------
A spatial Cliff-Ord-type Model with Heteroskedastic Innovations: Small and Large Sample Results, Department of Economics, University of Maryland'
Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances, Journal of Econometrics, forthcoming.
A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model, International Economic Review, 40, pages 509–533.
A Generalized Spatial Two Stage Least Square Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances, Journal of Real Estate Finance and Economics, 17, pages 99–121.
参见----------See Also----------
stslshac
stslshac
实例----------Examples----------
library(spdep)
data(columbus)
listw<-nb2listw(col.gal.nb)
res<-gstslshet(CRIME~HOVAL + INC, data=columbus, listw=listw)
summary(res)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 15:15:05
