R语言 vars包 SVAR()函数中文帮助文档(中英文对照)

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SVAR(vars)
SVAR()所属R语言包：vars

                                        Estimation of a SVAR
                                         估计的SVAR

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Estimates an SVAR (either "A-model", "B-model" or "AB-model") by using a scoring algorithm or by directly minimising the negative log-likelihood with optim().  
估计的的SVAR（无论是A-模型“，”B型“或”AB模型），由使用一个评分算法或直接减少的负对数似然optim()，。


用法----------Usage----------


SVAR(x, estmethod = c("scoring", "direct"), Amat = NULL, Bmat = NULL,
start = NULL, max.iter = 100, conv.crit = 0.1e-6, maxls = 1.0,
lrtest = TRUE, ...)
## S3 method for class 'svarest'
print(x, digits = max(3, getOption("digits") - 3), ...)



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：estmethod
Character, either scoring for estimating the SVAR-model with the scoring algorithm (default), or directly minimizing the negative log-likelihood.  
字符，无论是得分的得分算法（默认值），或者直接减少的负对数似然估计SVAR模型。


参数：start
Vector with starting values for the parameters to be optimised.   
向量的初始值进行优化的参数。


参数：lrtest
Logical, over-identification LR test, the result is set to NULL for just-identified system.
逻辑，过度识别LR测试，结果被设置为NULL刚刚发现的系统。


参数：max.iter
Integer, maximum number of iteration (used if estmethod = "scoring").
整数，最大迭代次数（使用如果estmethod = "scoring"）。


参数：conv.crit
Real, convergence value of algorithm (used if estmethod = "scoring").
真正的收敛值的算法（用于：如果estmethod = "scoring"）。


参数：maxls
Real, maximum movement of the parameters between two iterations of the scoring algorithm (used if estmethod = "scoring").
真正的，最大的两次迭代之间的得分算法（用于运动的参数，如果estmethod = "scoring"）。


参数：Amat
Matrix with dimension (K \times K) for A- or AB-model.
矩阵尺寸(K \times K)A-AB-模型。


参数：Bmat
Matrix with dimension (K \times K) for B- or AB-model.
矩阵尺寸(K \times K)B-AB-模型。


参数：digits
the number of significant digits to use when printing.
打印时所使用的数量显著数字。


参数：...
further arguments passed to or from other methods.   
进一步的参数传递给其他方法。


Details

详细信息----------Details----------

Consider the following structural form of a k-dimensional vector autoregressive model:
考虑下述结构形式的一个k维向量自回归模型：

The coefficient matrices (A_1^* | &hellip; | A_p^* | C^*) might now differ from the ones of a VAR (see ?VAR). One can now impose restrictions on "A" and/or "B", resulting in an "A-model" or "B-model" or if the restrictions are placed on both matrices, an "AB-model". In case of a SVAR "A-model", B = I_K and conversely for a SVAR "B-model". Please note that for either an "A-model" or "B-model", K(K-1)/2 restrictions have to be imposed, such that the models' coefficients are identified. For an "AB-model" the number of restrictions amounts to: K^2 + K(K-1)/2.<br> For an "A-model" a (K \times K) matrix has to be provided for the functional argument "Amat" and the functional argument "Bmat" must be set to "NULL" (the default). Hereby, the to be estimated elements of "Amat" have to be set as "NA". Conversely, for a "B-model" a matrix object with dimension (K \times K) with elements set to "NA" at the positions of the to be estimated parameters has to be provided and the functional argument "Amat" is "NULL" (the default). Finally, for an "AB-model" both arguments, "Amat" and "Bmat", have to be set as matrix objects containing desired restrictions and "NA" values. The parameters are estimated by minimising the negative of the concentrated log-likelihood function:  
的系数矩阵(A_1^* | &hellip; | A_p^* | C^*)现在可能会从VAR的不同（见?VAR）。现在可以限制对“A和/或B，A模式”或“B型”或如果被放置的限制这两个矩阵，AB模型“。在一个的SVAR“一个模型，B = I_K，反之为一个SVAR”B型“的情况。请注意，无论是A-模型“或”B模式“，K(K-1)/2不得不实行限制，模型的系数确定。对于“AB模型”的数量限制金额：K^2 + K(K-1)/2。<br>对于“A-模型的一个(K \times K)矩阵提供的功能参数” Amat的功能参数Bmat必须设置为“NULL（默认值）。由此，估计元素Amat有要被设置为NA。相反地，对于一个“B-模型的矩阵尺寸对象(K \times K)的元素设置为NA以进行估计的参数的位置，以提供与所述功能的参数Amat是NULL（默认值）。最后，AB模型参数，Amat和Bmat，已被设置为矩阵对象包含所需限制和NA 值。参数估计通过尽量减少其消极的浓缩的对数似然函数：

Two alternatives are implemented for this: a scoring algorithm or direct minimization with optim(). If the latter is chosen, the standard errors are returned if SVAR() is called with "hessian = TRUE".<br>
两种备选方案实施这样的：一个评分算法或直接最小化optim()。如果选择后者，标准差返回SVAR()被称为hessian = TRUE。<BR>的

If "start" is not set, then 0.1 is used as starting values for the unknown coefficients.<br>
如果start未设置，那么0.1使用的初始值未知系数。<BR>

The reduced form residuals can be obtained from the above equation via the relation: \bold{u}_t =   A^{-1}B\bold{\varepsilon}_t, with variance-covariance matrix &Sigma;_U = A^{-1}BB'A^{-1'}.<br>
从上述等式获得的还原形式残差可以通过下面的关系：\bold{u}_t =   A^{-1}B\bold{\varepsilon}_t，方差 - 协方差矩阵&Sigma;_U = A^{-1}BB'A^{-1'}。<br>物理化学学报

Finally, in case of an overidentified SVAR, a likelihood ratio statistic is computed according to:
最后，在过度识别SVAR的箱子中，根据计算的似然比统计量：

with \tilde{&Sigma;}_u^r being the restricted variance-covariance matrix and \tilde{&Sigma;}_u being the variance covariance matrix of the reduced form residuals. The test statistic is distributed as &chi;^2(nr - 2K^2 - \frac{1}{2}K(K + 1)), where nr is equal to the number of restrictions.  
与\tilde{&Sigma;}_u^r是受限制的方差 - 协方差矩阵和\tilde{&Sigma;}_u的简化形式残差的方差协方差矩阵。的检验统计量的分布作为&chi;^2(nr - 2K^2 - \frac{1}{2}K(K + 1))，其中nr是相等的数目的限制。


值----------Value----------

A list of class "svarest" with the following elements is returned:<br>
列表类的svarest返回包含下列元素：参考


参数：A
If A- or AB-model, the matrix of estimated coefficients.
如果A或AB模型中，矩阵的估计系数。


参数：Ase
The standard errors of "A".
A的标准误差。


参数：B
If A- or AB-model, the matrix of estimated coefficients.
如果A或AB模型中，矩阵的估计系数。


参数：Bse
The standard errors of "B".
B的标准误差。


参数：LRIM
For Blanchard-Quah estimation LRIM is the estimated long-run impact matrix; for all other SVAR models LRIM is NULL.   
布兰查德柯估计LRIM是为所有其他的SVAR模型估计术语影响矩阵; LRIM是NULL。


参数：Sigma.U
The variance-covariance matrix of the reduced form residuals times 100, i.e., &Sigma;_U = A^{-1}BB'A^{-1'}       \times 100.
方差 - 协方差矩阵的还原形式残差100倍，即，&Sigma;_U = A^{-1}BB'A^{-1'}       \times 100。


参数：LR
Object of class "htest", holding the Likelihood ratio overidentification test.
对象类的htest，拿着似然比过度识别检验。


参数：opt
List object returned by optim().
List对象返回optim()。


参数：start
Vector of starting values.
向量的初始值。


参数：type
SVAR-type, character, either "A-model", "B-model" or "AB-model".
SVAR型，性格，无论是A-模型“，”B型“或”AB模式“。


参数：var
The "varest" object "x".  
“varest对象x。


参数：iter
Integer, the count of iterations.  
整数，迭代计数。


参数：call
The call to SVAR().
call到SVAR()。


（作者）----------Author(s)----------


Bernhard Pfaff



参考文献----------References----------

Econometrics, 2nd edition, Springer, Berlin.
autoregressive modeling and impulse responses, in H. L眉tkepohl and M. Kr盲tzig (editors), Applied Time Series Econometrics, Cambridge University Press, Cambridge.
University Press, Princeton.
Analysis, Springer, New York.

参见----------See Also----------

VAR, SVEC, logLik, irf, fevd  
VAR，SVEC，logLik，irf，fevd


实例----------Examples----------


data(Canada)
var.2c <- VAR(Canada, p = 2, type = "const")
amat <- diag(4)
diag(amat) <- NA
amat[2, 1] <- NA
amat[4, 1] <- NA
## Estimation method scoring[＃估算方法得分]
SVAR(x = var.2c, estmethod = "scoring", Amat = amat, Bmat = NULL,
max.iter = 100, maxls = 1000, conv.crit = 1.0e-8)
## Estimation method direct[＃估算方法直接]
SVAR(x = var.2c, estmethod = "direct", Amat = amat, Bmat = NULL,
hessian = TRUE, method="BFGS")

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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