uGARCHfilter-class(rugarch)
uGARCHfilter-class()所属R语言包:rugarch
class: Univariate GARCH Filter Class
类别:单变量GARCH过滤器类
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Class for the univariate GARCH filter.
一类单变量GARCH过滤器。
扩展----------Extends----------
Class "GARCHfilter", directly. Class "rGARCH", by class "GARCHfilter", distance 2.
类"GARCHfilter",直接。类"rGARCH",类的“GARCHfilter”,距离2。
方法----------Methods----------
as.data.frame signature(x = "uGARCHfilter"): extracts the position (dates), data, fitted values, residuals and
as.data.framesignature(x = "uGARCHfilter"):提取物的位置(日期),数据,拟合值,残差
fitted signature(object = "uGARCHfilter"):
安装signature(object = "uGARCHfilter"):
residuals signature(object = "uGARCHfilter"):
残差signature(object = "uGARCHfilter"):
sigma signature(object = "uGARCHfilter"):
SIGMAsignature(object = "uGARCHfilter"):
coef signature(object = "uGARCHfilter"):
系数signature(object = "uGARCHfilter"):
infocriteria signature(object = "uGARCHfilter"):
infocriteriasignature(object = "uGARCHfilter"):
newsimpact signature(object = "uGARCHfilter"):
newsimpactsignature(object = "uGARCHfilter"):
likelihood signature(object = "uGARCHfilter"):
可能性signature(object = "uGARCHfilter"):
signbias signature(object = "uGARCHfilter"):
signbiassignature(object = "uGARCHfilter"):
gof signature(object = "uGARCHfilter", groups = "numeric"): calculates and returns the adjusted goodness of fit statistic and p-values for the fitted distribution based on the Vlaar and Palm paper (1993).
GOFsignature(object = "uGARCHfilter", groups = "numeric"):调整后的善良的拟合统计量和p值的拟合分布的Vlaar和掌讯通(1993)的基础上计算并返回。
persistence signature(object = "uGARCHfilter", pars = "missing", distribution = "missing", model = "missing", submodel = "missing"):
持久性signature(object = "uGARCHfilter", pars = "missing", distribution = "missing", model = "missing", submodel = "missing"):
halflife signature(object = "uGARCHfilter", pars = "missing", distribution = "missing", model = "missing"): calculates and returns the halflife of the garch fit variance given a
半衰期signature(object = "uGARCHfilter", pars = "missing", distribution = "missing", model = "missing"):计算并返回给定的GARCH模型拟合方差的半衰期
uncmean signature(object = "uGARCHfilter"): calculates and returns the unconditional mean of the conditional mean
uncmeansignature(object = "uGARCHfilter"):计算并返回的无条件均值的条件均值
uncvariance signature(object = "uGARCHfilter", pars = "missing", distribution = "missing", model = "missing", submodel = "missing"): calculates and returns the long run unconditional variance of the garch
uncvariance signature(object = "uGARCHfilter", pars = "missing", distribution = "missing", model = "missing", submodel = "missing"):计算并返回从长远来看无条件方差的GARCH
plot signature(x = "uGARCHfilter", y = "missing"):
图signature(x = "uGARCHfilter", y = "missing"):
show signature(object = "uGARCHfilter"):
显示signature(object = "uGARCHfilter"):
注意----------Note----------
The uGARCHfilter class contains almost all the methods available with the uGARCHfit with the exception of those requiring the scores of the likelihood (i.e the optimization process) such as the nyblom test.
uGARCHfilter类包含了几乎所有可用的方法的uGARCHfit,除了这些要求的可能性(即在优化过程),如nyblom测试的分数。
(作者)----------Author(s)----------
Alexios Ghalanos
实例----------Examples----------
## Not run: [#不运行:]
data(dji30ret)
ctrl = list(rho = 1, delta = 1e-8, outer.iter = 100, inner.iter = 650,
tol = 1e-6)
spec = ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1,1)),
mean.model = list(armaOrder = c(1,1), include.mean = TRUE),
distribution.model = "std")
sgarch.fit = ugarchfit(data = dji30ret[,"AA",drop=FALSE], spec = spec,
solver = "solnp", solver.control = ctrl)
spec = ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1,1)),
mean.model = list(armaOrder = c(1,1), include.mean = TRUE),
distribution.model = "std", fixed.pars = as.list(coef(sgarch.fit)))
sgarch.filter = ugarchfilter(data = dji30ret[,"AA",drop=FALSE], spec = spec)
c(likelihood(sgarch.filter), likelihood(sgarch.fit))
c(uncmean(sgarch.filter), uncmean(sgarch.fit))
c(uncvariance(sgarch.filter), uncvariance(sgarch.fit))
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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