arfimaroll-methods(rugarch)
arfimaroll-methods()所属R语言包：rugarch
function: ARFIMA Rolling Density Forecast and Backtesting
功能：ARFIMA滚动密度的预测及回溯测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Method for creating rolling density forecast from ARFIMA models with option
从ARFIMA模型“选项用于创建滚动密度预测方法
用法----------Usage----------
arfimaroll(spec, data, n.ahead = 1, forecast.length = 500, refit.every = 25,
refit.window = c("recursive", "moving"), parallel = FALSE,
parallel.control = list(pkg = c("multicore", "snowfall"), cores = 2),
solver = "solnp", fit.control = list(), solver.control = list(),
calculate.VaR = TRUE, VaR.alpha = c(0.01, 0.05), ...)
参数----------Arguments----------
参数：spec
An ARFIMA spec object specifiying the desired model for testing.
的ARFIMA的规范对象specifiying所需的模型进行测试。
参数：data
A univariate dataset.
单变量的数据集。
参数：n.ahead
The number of periods to forecast.
期预测。
参数：forecast.length
The length of the total forecast for which out of sample data from the dataset will be excluded for testing.
满分从数据集的样本数据的总预测的长度将被排除用于测试。
参数：refit.every
Determines every how many periods the model is re-estimated.
确定每个模型重新估计多少时间。
参数：refit.window
Whether the refit is done on an expanding window including all the previous data or a moving window, the length of the window determined by the argument above (refit.every).
是否改装扩大窗口中包括的所有以前的数据或一移动窗口，上述（refit.every）由参数确定的窗口的长度上完成。
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：solver
The solver to use.
求解器使用。
参数：fit.control
Control parameters parameters passed to the fitting function.
控制参数通过拟合函数。
参数：solver.control
Control parameters passed to the solver.
控制参数传递的解算器。
参数：calculate.VaR
Whether to calculate forecast Value at Risk during the estimation.
无论是计算预测值在评估风险。
参数：VaR.alpha
The Value at Risk tail level to calculate.
在风险尾水平的价值来计算。
参数：...
.
。
Details
详细信息----------Details----------
ARFIMA models generate a partially time varying density based on the variation in the conditional mean values (sigma, skewness and shape are not time varying). The function first generates rolling forecasts of the ARFIMA model and then rescales the density from a standardized (0, 1, skew, shape) to the one representing the underlying return process (mu, sigma, skew, shape). Given this information it is then a simple matter to generate any measure of risk through the analytical evaluation of some type of function of the density. The function calculates one such measure (VaR), but since the full density parameters are returned, the user can calculate many others.<br> The argument refit.every determines every how many periods the fit is recalculated and the total forecast length actually calculated. For example, for a forecast length of 500 and refit.every of 25, this is 20 windows of 25 periods each for a total actual forecast length of 500. However, for a refit.every of 30, we take the floor of the division of 500 by 30 which is 16 windows of 30 periods each for a total actual forecast length of 480 (16 x 30). The important thing to remember about the refit.every is that it acts like the n.roll argument in the arfimaforecast function as it determines the number of rolls to perform. For example for n.ahead of 1 and refit.every of 25, the forecast is rolled every day using the filtered (actual) data of the previous period while for n.ahead of 1 and refit.every of 1 we will get 1 n.ahead forecasts for every day after which the model is refitted and reforecast for a total of 500 refits (when length.forecast is 500)!<br>
ARFIMA模型产生的变化的基础上，在有条件的平均值（标准差，偏度和形状不随时间变化）部分随时间变化的密度。该函数首先产生的滚动预测的ARFIMA模型，然后重新调整密度从一个标准化的（0，1，倾斜，形状）的潜在回报（MU，SIGMA，倾斜，形状）。给定该信息，然后，它是一个简单的事情产生的风险的任何措施，通过分析评价的密度的函数的一些类型。该函数计算一个这样的措施（VAR），但由于全密度参数返回时，用户可以计算出其他许多。<BR>的参数refit.every决定每隔多少时间适合将重新计算实际计算和预测总长度。例如，对于500和refit.every 25预测长度，这是每25个周期的总的实际预测长度为500的20个窗口。然而，对于30一refit.every，我们采取的划分500 30 30期间是16个窗口，每一个的总实际的预测长度为480（16×30）的地板。重要的是要记住的refit.every的是，它的作用就像arfimaforecast功能的n.roll参数，因为它决定了轧辊执行。对于例如，和refit.every 25为n.ahead，预测推出每天使用过滤（实际）数据的前一段时间，而我们将得到n.ahead 1和refit.every的1 1 N每一天后，装复并重新预测模型共500整修（当length.forecast为500）。提前预测！<BR>
值----------Value----------
An object of class ARFIMAroll.
对象的类ARFIMAroll。
（作者）----------Author(s)----------
Alexios Ghalanos
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-28 23:33:41
