HLTest(rugarch)
HLTest()所属R语言包：rugarch
The Non-Parametric Density Test of Hong and Li
香港和李非参数密度测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Implements the Non-Parametric Density Test of Hong and Li.
实非参数密度测试，香港和李。
用法----------Usage----------
HLTest(PIT, lags = 4, kernel = "quartic", conf.level = 0.95)
参数----------Arguments----------
参数：PIT
This represents the actual data transformed into a U(0,1) series by applying the distribution function of the estimated model conditional on the parameters.
这表示通过施加条件的参数的估计模型的分布函数的实际的数据转化成U（0,1）系列。
参数：lags
The number of lags to use for testing the joint hypothesis.
的滞后阶数使用联合测试假设。
参数：kernel
The kernel to use for the comparison against the PIT series (only the "quartic" currently implemented).
内核使用比较对PIT系列（目前实施的“四次”）。
参数：conf.level
The confidence level at which the Null Hypothesis is evaluated.
在零假设被评估的置信水平。
Details
详细信息----------Details----------
A novel method to analyze how well a conditional density fits the underlying data is through the probability integral transformation (PIT) discussed in Rosenblatt (1952) and used in the BerkowitzTest. More recently, Hong and Li (2005) introduced a nonparametric portmanteau test, building on the work of Ait-Sahalia (1996), which tests the joint hypothesis of i.i.d and uniformity for a series of PIT transformed data. To achieve this, it tests for misspecification in the conditional moments of the model transformed standardized residuals, and is distributed as N(0, 1) under the Null of a correctly specified model. These moment tests are reported as "M(1,1)" to "M(4,4)" in the output, while "W" is the Portmanteu type test statistic for general misspecification (using p lags) and also distributed as N(0, 1) under the Null of a correctly specified model. Only upper tail critical values are used in this test. The interested reader is referred to the paper for more details.
一种新的方法来分析如何有条件的密度适合的基础数据是通过概率积分变换（PIT）讨论罗森布拉特（1952年）和在BerkowitzTest。最近，香港和Li（2005）推出了非参数的混成测试，AIT-Sahalia的联合假设独立同分布的均匀性，一连串的PIT转换后的数据（1996），测试工作的基础上。要做到这一点，它测试在有条件的时刻，标准化残差模型转化为设定错误，且分布N（0，1）正确的指定型号的空。这些时刻的测试报告为M（1,1）到M（4,4）的输出，而WPortmanteu的型式试验统计一般设定错误（使用p滞后）也有分布N（0，1）根据正确的指定型号的空。在这个测试中使用的仅上尾临界值。有关详细信息，有兴趣的读者可以参考的文件。
值----------Value----------
A list with the following items:<br>
以下项目：参考列表
参数：statistic
The individual moment and joint test statistics.
个别时刻和联合测试数据。
参数：Decision
Whether to reject or not the Null given the conf.level.
无论是拒绝还是不空的conf.level。
（作者）----------Author(s)----------
Alexios Ghalanos
参考文献----------References----------
rate, Review of Financial Studies, 9(2), 385–426.<br> Berkowitz, J. (2001), Testing density forecasts, with applications to risk management, Journal of Business and Economic Statistics, 19(4), 465–474.<br> Hong, Y., and Li, H. (2005), Nonparametric specification testing for continuous-time models with applications to term structure of interest rates, Review of Financial Studies, 18(1), 37–84.<br> Rosenblatt, M. (1952), Remarks on a multivariate transformation, The Annals of Mathematical Statistics, 23(3), 470–472.
实例----------Examples----------
## Not run: [＃不运行：]
data(dji30ret)
spec = ugarchspec(mean.model = list(armaOrder = c(1,1), include.mean = TRUE),
variance.model = list(model = "gjrGARCH"), distribution.model = "sstd")
fit = ugarchfit(spec, data = dji30ret[, 1, drop = FALSE])
z = residuals(fit)/sigma(fit)
PIT = pdist("sstd",z, mu = 0, sigma = 1, skew = coef(fit)["skew"],
shape=coef(fit)["shape"])
print(HLTest(PIT, lags=4))
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。
|
发表于 2012-9-28 23:36:57
