ugarchfit-methods(rugarch)
ugarchfit-methods()所属R语言包：rugarch
function: Univariate GARCH Fitting
功能：单变量GARCH配件
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Method for fitting a variety of univariate GARCH models.
各种单变量GARCH模型拟合方法。
用法----------Usage----------
ugarchfit(spec, data, out.sample = 0, solver = "solnp", solver.control = list(),
fit.control = list(stationarity = 1, fixed.se = 0, scale = 0), ...)
参数----------Arguments----------
参数：data
A univariate data object. Can be a numeric vector, matrix, data.frame, zoo, xts, timeSeries, ts or irts object.
一元数据对象。可以是一个数值向量，矩阵，数据框，动物园，XTS，时间序列，TS或IRTS对象。
参数：spec
A univariate GARCH spec object of class uGARCHspec.
单变量GARCH规范的对象的类uGARCHspec。
参数：out.sample
A positive integer indicating the number of periods before the last to keep for out of sample forecasting (see details).
一个正整数，表示前的最后期间保持了样本外预测（见详情）。
参数：solver
One of either “nlminb”, “solnp”, “lbfgs”, “gosolnp” or “nloptr”.
其中任“nlminb”，“solnp”中，“LBFGS”中，“gosolnp”或“nloptr”。
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. Stationarity explicitly imposes the variance stationarity constraint during optimization. The fixed.se argument controls whether standard errors should be calculated for those parameters which were fixed (through the fixed.pars argument of the ugarchspec function). The scale parameter controls whether the data should be scaled before being submitted to the optimizer.
控制参数传递给装修程序。在优化过程中的平稳性明确规定的方差平稳约束。的参数控制标准误差是否应计算这些参数是固定的（通过论点的ugarchspec function). The scale parameter controls whether the data should be scaled before being submitted to the optimizer.“的fixed.pars的fixed.se
参数：...
.
。
Details
详细信息----------Details----------
The GARCH optimization routine first calculates a set of feasible starting points which are used to initiate the GARCH recursion. The main part of the likelihood calculation is performed in C-code for speed.<br> The out.sample option is provided in order to carry out forecast performance testing against actual data. A minimum of 5 data points are required for these tests. If the out.sample option is positive, then the routine will fit only N - out.sample (where N is the total data length) data points, leaving out.sample points for forecasting and testing using the forecast performance measures. In the ugarchforecast routine the n.ahead may also be greater than the out.sample number resulting in a combination of out of sample data points matched against actual data and some without, which the forecast performance tests will ignore.<br> The “gosolnp” solver allows for the initialization of multiple restarts of the solnp solver with randomly generated parameters (see documentation in the Rsolnp-package for details of the strategy used). The solver.control list then accepts the following additional (to the solnp) arguments: “n.restarts” is the number of solver restarts required (defaults to 1), “parallel” and “parallel.control” for use of the parallel functionality, “rseed” is the seed to initialize the random number generator, and “n.sim” is the number of simulated parameter vectors to generate per n.restarts.
的的GARCH优化程序首先计算一组可行的出发点，这是用来启动GARCH递归。似然度计算的主要部分的C代码进行的速度。<br>该out.sample选项设置，以便进行测试的实际数据的预测性能。这些测试所需的最低的5个数据点。如果out.sample选项是正的，那么程序将适合：仅N - out.sample（其中N是总的数据长度）的数据点，用于预测和测试使用的预测性能的措施留下out.sample点。在ugarchforecast例程n.ahead也可以是更大的，比满分样本数据点的组合的实际数据和一些不相匹配，导致的out.sample数目的预测性能测试将忽略。<BR >“gosolnp”求解器可以初始化多个重新启动的solnp求解随机生成的参数（在Rsolnp包使用的策略的详细信息，请参阅文档）。该solver.control名单，然后接受下列额外的（solnp）参数：“n.restarts”是求解器重新启动需要使用（默认为1），“平行”和“parallel.control”平行的功能，“rseed”是初始化的随机数发生器的种子，并，“n.sim”是数字模拟的参数矢量，以产生每n.restarts。
值----------Value----------
A uGARCHfit object containing details of the GARCH fit.
AuGARCHfit对象，其中包含详细的GARCH适合。
注意----------Note----------
The nloptr solver takes the following options in the solver.control list:<br>
nloptr求解器采用以下选项在solver.control列表如下：<br>
</td><td align="left"> solver </td><td align="left"> the nloptr solver to use </td><td align="left"> default: 1 ("SBPLX"
</ TD> <TD ALIGN="LEFT">求解器</ TD> <TD ALIGN="LEFT"> nloptr求解器使用</ TD> <TD ALIGN="LEFT">默认：1（SBPLX“
The solver option for nloptr has 10 different choices (1:10), which are 1:"COBYLA", 2:"BOBYQA", 3:"PRAXIS", 4:"NELDERMEAD", 5:"SBPLX", 6:"AUGLAG"+"COBYLA", 7:"AUGLAG"+"BOBYQA", 8:"AUGLAG"+"PRAXIS", 9:"AUGLAG"+"NELDERMEAD" and 10:"AUGLAG"+"SBPLX". As always, your mileage will vary and care should be taken on the choice of solver, tuning parameters etc. If you do use this solver try 9 or 10 first.
求解选项for nloptr有10种不同的选择，（1:10），它们有1COBYLA，2BOBYQA，3：PRAXIS，4NELDERMEAD，5SBPLX ，6：AUGLAG+COBYLA，7AUGLAG+BOBYQA，8AUGLAG+PRAXIS，9：AUGLAG+NELDERMEAD和10：AUGLAG+SBPLX。与往常一样，您的里程可能有所不同，应小心求解器的选择上，调整参数等，如果你使用该解算器尝试9或10首。
（作者）----------Author(s)----------
Alexios Ghalanos
参见----------See Also----------
For specification ugarchspec,filtering ugarchfilter, forecasting ugarchforecast, simulation ugarchsim, rolling forecast and estimation ugarchroll, parameter distribution and uncertainty ugarchdistribution, bootstrap forecast ugarchboot.
的规范ugarchspec，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，引导预测ugarchboot。
实例----------Examples----------
# Basic GARCH(1,1) Spec[基本GARCH（1,1）规格]
data(dmbp)
spec = ugarchspec()
fit = ugarchfit(data = dmbp[,1], spec = spec)
fit
coef(fit)
head(as.data.frame(fit))
#plot(fit,which="all")[图（FIT =“所有”）]
# in order to use fpm (forecast performance measure function)[为了使用FPM（预测性能测量功能）]
# you need to select a subsample of the data:[你需要选择其中一个样本的数据：]
spec = ugarchspec()
fit = ugarchfit(data = dmbp[,1], spec = spec, out.sample=100)
forc = ugarchforecast(fit, n.ahead=100)
# this means that 100 data points are left from the end with which to[这意味着100个数据点，从端与左]
# make inference on the forecasts[推断的预测]
fpm(forc)
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 23:39:04
