TVAR(tsDyn)
TVAR()所属R语言包：tsDyn
Multivariate Threshold Autoregressive model
多元门限自回归模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate a multivariate Threshold VAR
估计一个多元的阈值VAR
用法----------Usage----------
TVAR(data, lag, include = c( "const", "trend","none", "both"), model=c("TAR", "MTAR"), commonInter=FALSE, nthresh=1,thDelay=1, mTh=1,thVar, trim=0.1,ngrid, gamma=NULL, around, plot=FALSE, dummyToBothRegimes=TRUE,trace=TRUE, trick="for", max.iter=2)
参数----------Arguments----------
参数：data
time series
时间序列
参数：lag
Number of lags to include in each regime
包括在每一个政权的滞后阶数
参数：include
Type of deterministic regressors to include
确定性回归变量的类型，包括
参数：model
Whether the transition variable is taken in levels (TAR) or difference (MTAR)
无论是过渡变量的水平（TAR）或差（mtAR的）
参数：commonInter
Whether the deterministic regressors are regime specific (commonInter=FALSE) or not.
无论是政权的具体（commonInter = FALSE）或不确定性回归量。
参数：nthresh
Number of thresholds
数阈值
参数：thDelay
'time delay' for the threshold variable (as multiple of embedding time delay d) PLEASE NOTE that the notation is currently different to univariate models in tsDyn. The left side variable is taken at time t, and not t+1 as in univariate cases.
“时间延迟”的阈值变量（d）请注意，目前不同的符号是单因素模型在tsDyn多个嵌入时间延迟。左侧变量是在时间t的，而不是在单变量的情况下，1吨。
参数：mTh
combination of variables with same lag order for the transition variable. Either a single value (indicating which variable to take) or a combination
具有相同的转换变量的滞后阶数为变量的组合。单个值（指示所采取的变量）或组合
参数：thVar
external transition variable
外部转换变量
参数：trim
trimming parameter indicating the minimal percentage of observations in each regime
微调参数表示的最小百分比在每一个政权的意见
参数：ngrid
number of elements of the grid, especially for nthresh=3
的网格中的元素的数量，尤其是对nthresh=3
参数：gamma
prespecified threshold values
预先设定的阈值
参数：around
The grid search is restricted to <VAR>ngrid</VAR> values around this point. Especially useful for nthresh=3.
网格搜索仅限于<VAR> ngrid </ VAR值围绕此点。特别有用为nthresh=3。
参数：plot
Whether a plot showing the results of the grid search should be printed
网格搜索的结果的曲线图，表示是否应打印
参数：dummyToBothRegimes
Whether the dummy in the one threshold model is applied to each regime or not.
无论在一个阈值模型的伪被施加到每个制度或不是。
参数：trace
should additional infos be printed out?
额外的相关信息被打印出来吗？
参数：trick
type of R function called: for or mapply
R函数的类型称为：for或mapply
参数：max.iter
Number of iterations for the algorithm
该算法中的迭代次数
Details
详细信息----------Details----------
For fixed th and threshold variable, the model is linear, so estimation can be done directly by CLS (Conditional Least Squares). The search of the parameters values is made upon a grid of potential values. So it is pretty slow.
固定th和阈值变量，模型是线性的，所以估计可以直接进行CLS（条件最小二乘）。时作出的可能值的网格的参数的值的搜索。因此，它是相当缓慢的。
nthresh=1: estimation of one threshold model (two regimes) upon a grid of <VAR>ngrid</VAR> values (default to ALL) possible thresholds and delays values.
，nthresh = 1：估计的一个阈值模型（制度）一格的<VAR> ngrid后</ VAR值（默认为全部）可能的阈值以及延迟值。
nthresh=2: estimation of two thresholds model (three regimes) Conditional on the threshold found in model where nthresh=1, the second threshold is searched. When both are found, a second grid search is made with 30 values around each threshold.
nthresh = 2：估计的两个阈值模型（三个政权）发现的模型，其中的= nthresh 1，第二阈值被搜索的阈值的条件下。当两个被发现，第二栅极搜索与30围绕每个阈值的值。
nthresh=3: DOES NOT estimate a 3 thresholds model, but a 2 thresholds model with a whole grid over the thresholds parameters (so is really slow) with a given delay, is there rather to check the consistency of the method nthresh=2
不nthresh = 3：估计3阈值模型，但2阈值模型与整个网格的阈值参数（这样是很慢的），给定的延迟，而检查一致性的方法nthresh = 2
值----------Value----------
An object of class TVAR, with standard methods.
TVAR的类的对象，标准方法。
（作者）----------Author(s)----------
Matthieu Stigler
参考文献----------References----------
参见----------See Also----------
lineVar for the linear VAR/VECM, TVAR.LRtest to test for TVAR, TVAR.sim to simulate/bootstrap a TVAR.
lineVar的线性VAR / VECM，TVAR.LRtest测试TVAR，TVAR.sim来模拟/启动TVAR。
实例----------Examples----------
data(zeroyld)
data<-zeroyld
TVAR(data, lag=2, nthresh=2, thDelay=1, trim=0.1, mTh=1, plot=TRUE)
##The one threshold (two regimes) gives a value of 10.698 for the threshold and 1 for the delay. Conditional on this values, the search for a second threshold (three regimes) gives 8.129. Starting from this values, a full grid search finds the same values and confims the first step estimation. [＃一个阈值（制度）给出了10.698的阈值和1的延迟值。这个值条件下，寻找第二阈值（3个制度）提供了8.129。从这个值，一个完整的的网格搜索发现相同的价值观和优化算法的第一步估计。]
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。
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发表于 2012-10-1 12:34:52
