ugarchboot-methods(rugarch)
ugarchboot-methods()所属R语言包：rugarch
function: Univariate GARCH Forecast via Bootstrap
通过引导的功能：单变量GARCH预测
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Method for forecasting the GARCH density based on a bootstrap procedures (see details and references).
，基于GARCH密度的自举程序（查看详细信息和参考文献）的预测方法。
用法----------Usage----------
ugarchboot(fitORspec, data = NULL, method = c(" artial", "Full"), n.ahead = 10,
n.bootfit = 100, n.bootpred = 500, out.sample = 0, rseed = NA, solver = "solnp",
solver.control = list(), fit.control = list(),
external.forecasts = list(mregfor = NULL, vregfor = NULL), parallel = FALSE,
parallel.control = list(pkg = c("multicore", "snowfall"), cores = 2))
参数----------Arguments----------
参数：fitORspec
Either a univariate GARCH fit object of class uGARCHfit or alternatively a univariate GARCH specification object of class uGARCHspec with valid parameters supplied via the setfixed<- function in the specification.
无论是单变量GARCH类uGARCHfit或者单变量GARCH规范的类的对象的合适的对象uGARCHspec提供有效的参数通过setfixed<-函数的规范。
参数：data
Required if a specification rather than a fit object is supplied.
被供给时需要的规范，而不是一个合适的对象。
参数：method
Either the full or partial bootstrap (see note).
无论是全部或部分自举（见注）。
参数：n.ahead
The forecast horizon.
预测水平。
参数：n.bootfit
The number of simulation based re-fits used to generate the parameter distribution (i.e the parameter uncertainty). Not relevant for the &ldquoartial” method.
仿真的数目根据重新适合用于生成参数的分布（即参数的不确定性）。不相关的“部分”的方法。
参数：n.bootpred
The number of bootstrap replications per parameter distribution per n.ahead forecasts used to generate the predictive density. If this is for the partial method, simply the number of random samples from the empirical distribution to generate per n.ahead.
每参数分布每n.ahead预测用于生成预测密度自举重复的数目。如果这是部分的方法，简单的随机样本的经验分布产生每n.ahead。
参数：out.sample
Optional. If a specification object is supplied, indicates how many data points to keep for out of sample testing.
可选。如果提供一个规范的对象，表示保留多少个数据点出样品测试。
参数：rseed
A vector of seeds to initialize the random number generator for the resampling with replacement method (if supplied should be equal to n.bootfit + n.bootpred).
一种向量，种子初始化随机数发生器，用于替换方法（如果提供的话应该等于以n.bootfit + n.bootpred），再采样。
参数：solver
One of either “nlminb” or “solnp”.
之一“nlminb”或“solnp”。
参数：solver.control
Control arguments list passed to optimizer.
控制参数列表传递给优化。
参数：fit.control
Control arguments passed to the fitting routine (as in the ugarchfit method).
控制参数传递到配件常规（如在ugarchfit方法）。
参数：external.forecasts
A list with forecasts for the external regressors in the mean and/or variance equations if specified.
如果指定的平均值和/或方差方程外部回归预测的列表。
参数：parallel
Whether to make use of parallel processing on multicore systems.
是否利用多核系统上的并行处理。
参数：parallel.control
The parallel control options including the type of package for performing the parallel calculations ("multicore" for non-windows O/S and "snowfall" for all O/S), and the number of cores to make use of.
并行控制选项，包括包的类型进行并行计算（多核非Windows O / S和“降雪”，所有的O / S），核心数量的利用。
参数：...
.
。
Details
详细信息----------Details----------
There are two main sources of uncertainty about n.ahead forecasting from GARCH models, namely that arising from the form of the predictive density and due to parameter estimation. The bootstrap method considered here, is based on resampling innovations from the empirical distribution of the fitted GARCH model to generate future realizations of the series and sigma. The “full” method, based on the referenced paper by Pascual et al, takes into account parameter uncertainty by building a simulated distribution of the parameters through simulation and refitting. This process, while more accurate, is very time consuming which is why the parallel option (as in the ugarchdistribution is available and recommended). The “partial” method, only considers distribution uncertainty and while faster, will not generate prediction intervals for the sigma 1-ahead forecast for which only the parameter uncertainty is relevant in GARCH type models.
有两个n.ahead预测的不确定性的主要来源，即从GARCH模型的预测密度和参数估计的形式产生的。这里考虑的，是Bootstrap方法的基础上，重采样创新的经验分布GARCH模型的拟合产生的系列产品和sigma的未来实现。帕斯夸尔等人被引用的论文的基础上，“全面”的方法，需要考虑参数的不确定性建立一个模拟分布的参数，通过仿真和改装。 ，更准确，但这个过程是非常耗时的，这就是为什么平行的选项（ugarchdistribution在提供，并建议）。 “局部”的方法，只考虑分布的不确定性，而速度更快，不会产生西格玛1超前预测的参数不确定性是相关的GARCH型模型的预测区间。
值----------Value----------
A uGARCHboot object containing details of the GARCH bootstrapped forecast density.
AuGARCHboot对象，其中包含细节的GARCH自举预测密度。
（作者）----------Author(s)----------
Alexios Ghalanos
参考文献----------References----------
ARIMA processes, Journal of Time Series Analysis.<br> Pascual, L., Romo, J. and Ruiz, E. 2006, Bootstrap prediction for returns and volatilities in GARCH models, Computational Statistics and Data Analysis.<br>
参见----------See Also----------
For specification ugarchspec, fitting ugarchfit, filtering ugarchfilter, forecasting ugarchforecast, simulation ugarchsim, rolling forecast and estimation ugarchroll, parameter distribution and uncertainty ugarchdistribution.
的规范ugarchspec，装修ugarchfit，过滤ugarchfilter，预测ugarchforecast，模拟ugarchsim，滚动预测和估计ugarchroll，参数分布和不确定性ugarchdistribution。
实例----------Examples----------
## Not run: [＃不运行：]
data(dji30ret)
spec = ugarchspec(variance.model=list(model="gjrGARCH", garchOrder=c(1,1)),
mean.model=list(armaOrder=c(1,1), arfima=FALSE, include.mean=TRUE,
archm = FALSE, archpow = 1), distribution.model="std")
ctrl = list(tol = 1e-7, delta = 1e-9)
fit = ugarchfit(data=dji30ret[, "BA", drop = FALSE], out.sample = 0,
spec = spec, solver = "solnp", solver.control = ctrl,
fit.control = list(scale = 1))
bootpred = ugarchboot(fit, method = "artial", n.ahead = 120, n.bootpred = 2000)
bootpred
# as.data.frame(bootpred, which = "sigma", type = "q", qtile = c(0.01, 0.05))[as.data.frame（bootpred，“西格玛”，类型为“Q”，qtile = C（0.01，0.05））]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-28 23:38:20
