causality(vars)
causality()所属R语言包：vars
Causality Analysis
因果分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the test statistics for Granger- and Instantaneous causality for a VAR(p).
格兰杰和瞬时因果关系的VAR（P）计算检验统计量。
用法----------Usage----------
causality(x, cause = NULL, vcov.=NULL, boot=FALSE, boot.runs=100)
参数----------Arguments----------
参数：x
Object of class "varest"; generated by VAR().
类的varest的对象，所产生的VAR()。
参数：cause
A character vector of the cause variable(s). If not set, then the variable in the first column of x$y is used as cause variable and a warning is printed.
字符向量的原因变量（S）。如果没有设置，那么x$y的第一列中的变量是作为原因变量和打印一个警告。
参数：vcov.
a specification of the covariance matrix of the estimated coefficients. This can be specified as a matrix or as a function yielding a matrix when applied to x.
的协方差矩阵的估计系数的规格。这可以被指定为一个矩阵或作为一个功能时产生一个矩阵施加到x。
参数：boot
Logical. Whether a wild bootstrap procedure should be used to compute the critical values. Default is no
逻辑。野生自举程序是否应该被用来计算的临界值。默认是不启用
参数：boot.runs
Number of bootstrap replications if boot=TRUE
引导复制的数量，如果启动= TRUE
Details
详细信息----------Details----------
Two causality tests are implemented. The first is a F-type Granger-causality test and the second is a Wald-type test that is characterized by testing for nonzero correlation between the error processes of the cause and effect variables. For both tests the vector of endogenous variables \bold{y}_t is split into two subvectors \bold{y}_{1t} and \bold{y}_{2t} with dimensions (K_1 \times 1) and (K_2 \times 1) with K = K_1 + K_2.<br> For the rewritten VAR(p):
两个因果关系检验的贯彻落实。第一个是F型Granger因果测试和第二是一个沃尔德型测试，其特征在于通过测试为非零的原因和影响变量之间的相关性的错误过程。对于这两个测试向量的内生变量\bold{y}_t分裂成两个子向量\bold{y}_{1t}和\bold{y}_{2t}的尺寸(K_1 \times 1)和(K_2 \times 1)K = K_1 + K_2。<无线电通信>对于重写的VAR（对）：
the null hypothesis that the subvector \bold{y}_{1t} does not Granger-cause \bold{y}_{2t}, is defined as \bold{α}_{21, i} = 0 for i = 1, 2, …, p. The alternative is: \exists \; \bold{α}_{21,i} \ne 0 for i = 1, 2, …, p. The test statistic is distributed as F(p K_1 K_2, KT - n^*), with n^* equal to the total number of parameters in the above VAR(p) (including deterministic regressors).<br> The null hypothesis for instantaneous causality is defined as: H_0: C \bold{σ} = 0, where C is a (N \times K(K + 1)/2) matrix of rank N selecting the relevant co-variances of \bold{u}_{1t} and \bold{u}_{2t}; \bold{σ} = vech(Σ_u). The Wald statistic is defined as:
的零假设，即的子向量\bold{y}_{1t}不格兰杰原因\bold{y}_{2t}，被定义为\bold{α}_{21, i} = 0i = 1, 2, …, p。另一种方法是：\exists \; \bold{α}_{21,i} \ne 0i = 1, 2, …, p。检验统计量分布F(p K_1 K_2, KT - n^*)，n^*等于总数的参数在上面的VAR（P）（包括确定性的回归系数）。参考瞬时因果关系的零假设被定义为：H_0: C \bold{σ} = 0，其中C是(N \times K(K + 1)/2)矩阵排名N选择有关合作的差异\bold{u}_{1t}和\bold{u}_{2t}，<X >。 Wald统计量定义为：
hereby assigning the Moore-Penrose inverse of the duplication matrix D_K with D_{K}^{+} and \tilde{Σ}_u = \frac{1}{T}∑_{t=1}^T \hat{\bold{u}}_t \hat{\bold{u}}_t'. The duplication matrix D_K has dimension (K^2 \times \frac{1}{2}K(K + 1)) and is defined such that for any symmetric (K \times K) matrix A, vec(A) = D_K vech(A) holds. The test statistic λ_W is asymptotically distributed as χ^2(N).
在此指定的Moore-Penrose逆矩阵的重复D_K与D_{K}^{+}和\tilde{Σ}_u = \frac{1}{T}∑_{t=1}^T \hat{\bold{u}}_t \hat{\bold{u}}_t'。重复矩阵D_K尺寸(K^2 \times \frac{1}{2}K(K + 1))和定义，任何对称(K \times K)矩阵A，vec(A) = D_K vech(A)持有。检验统计量λ_W渐近分布χ^2(N)。
Fot the Granger causality test, a robust covariance-matrix estimator can be used in case of heteroskedasticity through argument vcov. It can be either a pre-computed matrix or a function for extracting the covariance matrix. See vcovHC from package sandwich for further details.
FOT的格兰杰因果关系检验，稳健的协方差矩阵估计可以用于异方差性的情况下，通过参数vcov.它可以是预先计算的矩阵或提取的协方差矩阵的功能。见vcovHC包sandwich为进一步的细节。
A wild bootstrap computation (imposing the restricted model as null) of the p values is available through argument boot and boot.runs following Hafner and Herwartz (2009).
的野生的引导计算（空）施加限制的模型的p值，可通过参数boot和boot.runs以下哈夫纳和Herwartz的（2009年）。
值----------Value----------
A list with elements of class "htest":<br>
列表元素的类的htest参考：
参数：Granger
The result of the Granger-causality test.
的格兰杰因果关系检验的结果。
参数：Instant
The result of the instantaneous causality test.
瞬时因果检验的结果。
注意----------Note----------
The Moore-Penrose inverse matrix is computed with the function ginv contained in the package "MASS".<br> The Granger-causality test is problematic if some of the variables are nonstationary. In that case the usual asymptotic distribution of the test statistic may not be valid under the null hypothesis.
Moore-Penrose逆矩阵计算的功能ginv中包含的包MASS。参考格兰杰因果关系检验是有问题的，如果一些变量都是非平稳的。在这种情况下，通常的检验统计量的渐近分布的零假设下是无效的。
（作者）----------Author(s)----------
Bernhard Pfaff
参考文献----------References----------
econometric models and cross-spectral methods, Econometrica, 37: 424-438.
dynamics under multivariate generalized autoregressive heteroskedasticity, Statistica Neerlandica, 63: 294-323
University Press, Princeton.
Analysis, Springer, New York.
Statistics with S, 4th edition, Springer, New York.
Journal of Statistical Software, 16, 1-16
参见----------See Also----------
VAR
VAR
实例----------Examples----------
data(Canada)
var.2c <- VAR(Canada, p = 2, type = "const")
causality(var.2c, cause = "e")
#use a robust HC variance-covariance matrix for the Granger test:[使用强大的HC方差 - 协方差矩阵的Granger因果检验：]
causality(var.2c, cause = "e", vcov.=vcovHC(var.2c))
#use a wild-bootstrap procedure to for the Granger test[使用野生引导程序的Granger因果检验]
## Not run: causality(var.2c, cause = "e", boot=TRUE, boot.runs=1000)[＃不运行：的因果（var.2c，“E”，引导原因= TRUE，boot.runs = 1000）]
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注：
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发表于 2012-10-1 14:28:06
