ca.jo(urca)
ca.jo()所属R语言包：urca
Johansen Procedure for VAR
约翰森程序VAR
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Conducts the Johansen procedure on a given data set. The "trace" or "eigen" statistics are reported and the matrix of eigenvectors as well as the loading matrix.
在一个给定的数据集进行约翰森程序。 "trace"或"eigen"统计被报告和特征向量的矩阵，以及装载矩阵。
用法----------Usage----------
ca.jo(x, type = c("eigen", "trace"), ecdet = c("none", "const", "trend"), K = 2,
spec=c("longrun", "transitory"), season = NULL, dumvar = NULL)
参数----------Arguments----------
参数：x
Data matrix to be investigated for cointegration.
数据矩阵的协整关系。
参数：type
The test to be conducted, either "eigen" or "trace".
进行测试，无论是eigen或trace。
参数：ecdet
Character, "none" for no intercept in cointegration, "const" for constant term in cointegration and "trend" for trend variable in cointegration.
字符，none没有拦截的协整“，”const为常数项的协整和“trend的趋势变量的协整关系。
参数：K
The lag order of the series (levels) in the VAR.
系列（水平）的VAR的滞后阶数。
参数：spec
Determines the specification of the VECM, see details below.
确定的VECM的规格，详情如下。
参数：season
If seasonal dummies should be included, the data frequency must be set accordingly, i.e "4" for quarterly data.
如果应包括季节性假人，数据的频率必须进行相应的设置，即4季度数据。
参数：dumvar
If dummy variables should be included, a matrix with row dimension equal to x can be provided.
如果虚拟变量应包括，与行维度等于x的矩阵可以提供。
Details
详细信息----------Details----------
Given a general VAR of the form:
一般的VAR的形式：
the following two specifications of a VECM exist:
以下两种规格的VECM存在：
where
哪里
and
和
The \bold{Γ}_i matrices contain the cumulative long-run impacts, hence if spec="longrun" is choosen, the above VECM is estimated.
\bold{Γ}_i矩阵包含了术语的累积影响，因此，如果spec="longrun"选择，VECM估计。
The other VECM specification is of the form:
其他VECM模型规范的形式是：
where
哪里
and
和
The \bold{Π} matrix is the same as in the first specification. However, the \bold{Γ}_i matrices now differ, in the sense that they measure transitory effects, hence by setting spec="transitory" the second VECM form is estimated. Please note that inferences drawn on \bold{Π} will be the same, regardless which specification is choosen and that the explanatory power is the same, too.
\bold{Π}矩阵第一说明书中是相同的。然而，\bold{Γ}_i矩阵不同，在这个意义上，他们测量了短暂的效果，因此通过设置spec="transitory"第二VECM形式估计。请注意，推论\bold{Π}将是相同的，而不管其规格选择的解释力是一样的，。
If "season" is not NULL, centered seasonal dummy variables are included.
如果"season"是不为NULL，中心的季节虚拟变量也包括在内。
If "dumvar" is not NULL, a matrix of dummy variables is included in the VECM. Please note, that the number of rows of the matrix containing the dummy variables must be equal to the row number of x.
"dumvar"如果不为NULL，矩阵的虚拟变量的向量误差修正模型。请注意，包含虚设变量矩阵的行的数目必须是等于x的行号。
Critical values are only reported for systems with less than 11 variables and are taken from Osterwald-Lenum.
的临界值只报告系统的不到11个变量中，，并采取从Osterwald-Lenum的关系。
值----------Value----------
An object of class ca.jo.
对象的类ca.jo。
（作者）----------Author(s)----------
Bernhard Pfaff
参考文献----------References----------
Journal of Economic Dynamics and Control, 12, 231–254.
Inference on Cointegration – with Applications to the Demand for Money, Oxford Bulletin of Economics and Statistics, 52, 2, 169–210.
Cointegration Vectors in Gaussian Vector Autoregressive Models, Econometrica, Vol. 59, No. 6, 1551–1580.
Distribution of the Maximum Likelihood Cointegration Rank Test Statistics, Oxford Bulletin of Economics and Statistics, 55, 3, 461–472.
参见----------See Also----------
plotres, alrtest, ablrtest, blrtest, cajolst, cajools, lttest, ca.jo-class and urca-class.
plotres，alrtest，ablrtest，blrtest，cajolst，cajools，lttest，ca.jo-class和urca-class。
实例----------Examples----------
data(denmark)
sjd <- denmark[, c("LRM", "LRY", "IBO", "IDE")]
sjd.vecm <- ca.jo(sjd, ecdet = "const", type="eigen", K=2, spec="longrun",
season=4)
summary(sjd.vecm)
#[]
data(finland)
sjf <- finland
sjf.vecm <- ca.jo(sjf, ecdet = "none", type="eigen", K=2,
spec="longrun", season=4)
summary(sjf.vecm)
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。
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发表于 2012-10-1 13:34:18
