R语言 rugarch包 ugarchspec-methods()函数中文帮助文档(中英文对照)

loveR · 发表于 2012-9-28 23:40:55

ugarchspec-methods(rugarch)
ugarchspec-methods()所属R语言包：rugarch

 function: Univariate GARCH Specification
 功能：单变量GARCH规范

 译者：生物统计家园网机器人LoveR

描述----------Description----------

Method for creating a univariate GARCH specification object prior to fitting.
创建一个单变量GARCH规范对象之前配件的方法。

用法----------Usage----------

ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1),
submodel = NULL, external.regressors = NULL, variance.targeting = FALSE),
mean.model = list(armaOrder = c(1, 1), include.mean = TRUE, archm = FALSE,
archpow = 1, arfima = FALSE, external.regressors = NULL, archex = FALSE),
distribution.model = "norm", start.pars = list(), fixed.pars = list(), ...)

参数----------Arguments----------

参数：variance.model
List containing the variance model specification: model Valid models (currently implemented) are “sGARCH”, “fGARCH”, “eGARCH”, “gjrGARCH”, “apARCH” and “iGARCH”. garchOrder The ARCH (q) and GARCH (p) orders. submodel If the model is “fGARCH”, valid submodels are “GARCH”, “TGARCH”, “AVGARCH”, “NGARCH”, “NAGARCH”, “APARCH”,“GJRGARCH” and “ALLGARCH”. external.regressors A matrix object containing the external regressors to include in the variance equation with as many rows as will be included in the data (which is passed in the fit function). variance.targeting Indicates whether to use variance targeting for the sigma intercept “omega”.).
列表，其中包含的方差型号规格： 的的model有效的模型目前实施的是“sGARCH”中，“fGARCH”，“EGARCH”中，“gjrGARCH”中，“apARCH”和“IGARCH” 。参考garchOrderARCH（q）和GARCH（P）的订单。 submodel如果模式是“fGARCH”的，有效的子模型是GARCH“，”TGARCH“ ，“AVGARCH”，“NGARCH”中，“NAGARCH”，“APARCH”中，“GJRGARCH”和“ALLGARCH”。 external.regressorsA矩阵对象，其中包含外部的回归系数，包括在方差方程与尽可能多的行，将包含在数据（这是通过在拟合函数）。 variance.targeting指示是否使用方差为目标的sigma拦截“欧米茄”）。

参数：mean.model
List containing the mean model specification: armaOrder The autoregressive (ar) and moving average (ma) orders (if any). include.mean Whether to include the mean. archm Whether to include ARCH volatility in the mean regression. archpow Indicates whether to use st.deviation (1) or variance (2) in the ARCH in mean regression. arfima Whether to fractional differencing in the ARMA regression. external.regressors A matrix object containing the external regressors to include in the mean equation with as many rows as will be included in the data (which is passed in the fit function). archex (integer) Whether to multiply the last 'archex' external regressors by the conditional standard deviation.
List，其中包含的平均型号规格： armaOrder自回归（AR）和移动平均线（MA）的订单（如果有的话）。参考include.mean是否包含的意思。 archm是否包括ARCH波动，均值回归。 archpow表示是否使用st.deviation，（1）或ARCH均值回归的方差（2）。参考<X >无论是在ARMA回归分数差分。 arfima包含外部的回归量包括在均值方程与尽可能多的行，将包括一个matrix对象中的数据（这是通过在合适的功能）。参考external.regressors（整数）是否乘的最后的“archex的外部回归系数的条件标准偏差。

参数：distribution.model
The conditional density to use for the innovations. Valid choices are “norm” for the normal distibution, “snorm” for the skew-normal distribution, “std” for the student-t, “sstd” for the skew-student, “ged” for the generalized error distribution, “sged” for the skew-generalized error distribution, “nig” for the normal inverse gaussian distribution, “ghyp” for the Generalized Hyperbolic, and “jsu” for Johnson's SU distribution. Note that some of the distributions are taken from the fBasics package and implenented locally here for convenience. The “jsu” distribution is the reparametrized version from the “gamlss” package.
使用创新的条件密度。有效的选择是“规范”的正常发行套装，“的斜正态分布snorm”，“标准”-T的学生，“SSTD”的斜学生，“GED的广义”误差分布的斜广义误差分布，“NIG”的正态逆高斯分布的广义双曲的“ghyp”，和“JSU”约翰逊的SU分布的“sged”。请注意，从fBasics包采取一些分布和implenented本地这里为方便起见。从的“gamlss的”包的“JSU”的分布是reparametrized版本。

参数：start.pars
List of staring parameters for the optimization routine. These are not usually required unless the optimization has problems converging.
盯着参数的优化程序的列表。这些通常不是必需的，除非优化问题融合。

参数：fixed.pars
List of parameters which are to be kept fixed during the optimization. It is possible that you designate all parameters as fixed so as to quickly recover just the results of some previous work or published work. The optional argument “fixed.se” in the ugarchfit function indicates whether to calculate standard errors for those parameters fixed during the post optimization stage.
这是在优化过程中保持固定的参数列表。这可能是你指定的所有参数固定为快速恢复的结果，以前的一些工作或发表的作品。 ugarchfit功能的的“fixed.se”可选参数表示是否计算标准误差参数固定在优化阶段后。

参数：...
.
。

Details

详细信息----------Details----------

The specification allows for a wide choice in univariate GARCH models, distributions, and mean equation modelling. For the “fGARCH” model, this represents Hentschel's omnibus model which subsumes many others. For the mean equation, ARFIMAX is fully supported in fitting, forecasting and simulation. There is also an option to multiply the external regressors by the conditional standard deviation, which may be of use for example in calculating the correlation coefficient in a CAPM type setting. The “iGARCH” implements the integrated GARCH model. For the “EWMA” model just set “omega” to zero in the fixed parameters list. The asymmetry term in the rugarch package, for all implemented models, follows the order of the arch parameter alpha. Variance targeting, referred to in Engle and Mezrich (1996), replaces the intercept “omega” in the variance equation by 1 minus the persistence multiplied by the unconditional variance which is calculated by its sample counterpart in the squared residuals during estimation. In the presence of external regressors in the variance equation, the sample average of the external regresssors is multiplied by their coefficient and subtracted from the variance target. In order to understand which parameters can be entered in the start.pars and fixed.pars optional arguments, the list below exposes the names used for the parameters across the various models

note that when a parameter is followed by a number, this represents the order of the model. Just increment the number for higher orders): Mean Model: 
规范允许一个广泛的选择，单变量GARCH模型，分布，均值方程模型。对于的“fGARCH”的模式，这代表汉努瑶的综合性模型，它包含了许多其他人。的均值方程 有关，ARFIMAX是完全支持的拟合，预测和模拟。还有一个选项，外部回归系数相乘的条件标准差，这可能是使用例如，在计算相关系数在CAPM类型设置。 “IGARCH”实现了综合GARCH模型。对于“EWMA”的模式设置为“欧米茄”零在固定的参数列表。参考的不对称性术语在rugarch包，对所有执行的模型，如下的顺序拱参数alpha。 差异定位，简称在Engle和Mezrich（1996），取代方差方程的截距的“欧米茄”减去1的无条件方差的计算方法是对应的样品在估算过程中的残差平方乘以持久性。在外部回归量的方差方程中的存在下，它们的系数乘以外部regresssors样品平均减去从方差目标 为了理解可以在start.pars输入的参数和固定。 PARS可选参数，下面的列表中暴露出的各种型号的参数在使用的名称：（请注意，当一个参数后面跟一个数字，这代表了秩序的典范。只是增加更高的订单数）：平均型号：参考

Distribution Model: 
分布模型：参考

Variance Model (common specs): 
方差模型（通用规格）：参考

Variance Model (GJR, EGARCH): 
方差模型（GJR，EGARCH）：参考

Variance Model (APARCH): 
方差模型（APARCH）：参考

Variance Model (FGARCH): 
方差的模型（FGARCH）：参考

值----------Value----------

A uGARCHspec object containing details of the GARCH specification.
AuGARCHspec对象，其中包含的GARCH规格的详细信息。

（作者）----------Author(s)----------

Alexios Ghalanos

实例----------Examples----------

# a standard specification[标准规范]
spec1 = ugarchspec()
spec1
# an example which keep the ar1 and ma1 coefficients fixed:[一个例子，它保持固定的AR1和MA1系数：]
spec2 = ugarchspec(mean.model=list(armaOrder=c(2,2),
fixed.pars=list(ar1=0.3,ma1=0.3)))
spec2
# an example of the EWMA Model[EWMA模型的一个例子的]
spec3 = ugarchspec(variance.model=list(model="iGARCH", garchOrder=c(1,1)),
mean.model=list(armaOrder=c(0,0), include.mean=TRUE),
distribution.model="norm", fixed.pars=list(omega=0))

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