R语言 tsDyn包 SETAR()函数中文帮助文档(中英文对照)

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SETAR(tsDyn)
SETAR()所属R语言包：tsDyn

                                        Self Threshold Autoregressive model
                                         自门限自回归模型

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Self Exciting Threshold AutoRegressive model.
自激励门限自回归模型。


用法----------Usage----------


setar(x, m, d=1, steps=d, series, mL, mM, mH, thDelay=0, mTh, thVar, th, trace=FALSE, nested=FALSE, include = c( "const", "trend","none", "both"), common=c("none", "include","lags", "both"), model=c("TAR", "MTAR"), ML=seq_len(mL), MM=seq_len(mM), MH=seq_len(mH),nthresh=1,trim=0.15, type=c("level", "diff", "ADF"), restriction=c("none","OuterSymAll","OuterSymTh") )



用法----------Usage----------

setar(x, m, d=1, steps=d, series, mL=m, mH=m, thDelay=0, th,
trace=FALSE,include = c( "const", "trend","none", "both"),
common=FALSE, model=c("TAR", "MTAR"), ML=seq_len(mL), MH=seq_len(mH),nthresh=1,trim=0.15,
type=c("level", "diff", "ADF"), restriction=c("none","OuterSymAll","OuterSymTh") )

setar(x, m, d=1, steps=d, series, mL=m, mH=m, mTh, th,
trace=FALSE,include = c( "const", "trend","none", "both"),
common=FALSE, model=c("TAR", "MTAR"), ML=seq_len(mL), MH=seq_len(mH),nthresh=1,trim=0.15,
type=c("level", "diff", "ADF"), restriction=c("none","OuterSymAll","OuterSymTh") )

setar(x, m, d=1, steps=d, series, mL=m, mH=m, thVar, th,
trace=FALSE,include = c( "const", "trend","none", "both"),
common=FALSE, model=c("TAR", "MTAR"), ML=seq_len(mL), MH=seq_len(mH),nthresh=1,trim=0.15,
type=c("level", "diff", "ADF"), restriction=c("none","OuterSymAll","OuterSymTh") )



参数----------Arguments----------

参数：x
time series  
时间序列


参数：m, d, steps
embedding dimension, time delay, forecasting steps  
嵌入维，时间延迟，预测的步骤


参数：series
time series name (optional)  
时间序列的名称（可选）


参数：mL,mH
autoregressive order for "low" (mL) "middle" (mM, only useful if nthresh=2) and "high" (mH)regime (default values: m). Must be <=m. Alternatively, you can specify ML
自回归阶为“低”（mL）的“中间”（MM，唯一有用的，如果nthresh = 2）和“高”（MH）制度（默认值：M）。必须<= M。或者，您也可以指定ML


参数：thDelay
'time delay' for the threshold variable (as multiple of embedding time delay d)
“时间延迟”的阈值变量（如多个嵌入时间延迟d）


参数：mTh
coefficients for the lagged time series, to obtain the threshold variable
的滞后时间序列的系数，以获得阈值变量


参数：thVar
external threshold variable
外部的阈值变量


参数：th
threshold value (if missing, a search over a reasonable grid is tried)
阈值（如果丢失，搜索尝试在一个合理的电网）


参数：trace
should additional infos be printed? (logical)
额外的相关信息进行打印呢？ （逻辑）


参数：include
Type of deterministic regressors to include
确定性回归变量的类型，包括


参数：common
Indicates which elements are common to all regimes: no, only the include variables, the lags or both
表示哪些元素是共同所有的制度：没有，只有include变量，滞后或两个


参数：ML,MH
vector of lags for  order for "low" (ML) "middle" (MM, only useful if nthresh=2) and "high" (MH)regime. Max must be <=m
向量的滞后为“低”（ML）为了“中间”（MM，唯一有用的，如果nthresh = 2）和“高”（MH）制度。最大必须<=米


参数：model
Currently not implemented
目前尚未实现


参数：nthresh
Number of threshold of the model
模型的阈值的数目


参数：trim
trimming parameter indicating the minimal percentage of observations in each regime. Default to 0.15
微调参数表示的最小百分比在每一个政权的意见。默认为0.15


参数：type
Whether the variable is taken is level, difference or a mix (diff y= y-1, diff lags) as in the ADF test
在ADF检验变量是否是水平，差异性或混合（差异Y = Y-1，比较滞后）


参数：restriction
Restriction on the threshold. OuterSymAll will take a symmetric threshold and symmetric coefficients for outer regimes. OuterSymTh currently unavailable
限制在阈值上。 OuterSymAll将采取对称的阈值和外部制度的对称系数。 OuterSymTh目前不能使用。


参数：...
further arguments to be passed to nlar
被传递nlar的进一步的论据


Details

详细信息----------Details----------

Self Exciting Threshold AutoRegressive model.
自激励门限自回归模型。

with <VAR>z</VAR> the threshold variable. The threshold variable can alternatively be specified by (in that order):
，与<VAR> Z </ VAR>变量的阈值。阈值变量，也可以指定（按顺序）：




thDelay  z[t] = x[t - thDelay*d ]
thDelay z[t] = x[t - thDelay*d ]




mTh  z[t] = x[t] mTh[1] + x[t-d] mTh[2] + ... + x[t-(m-1)d] mTh[m]
MTH z[t] = x[t] mTh[1] + x[t-d] mTh[2] + ... + x[t-(m-1)d] mTh[m]




thVar  z[t] = thVar[t]
thVar z[t] = thVar[t]

For fixed th and threshold variable, the model is linear, so phi1 and phi2 estimation can be done directly by CLS (Conditional Least Squares). Standard errors for phi1 and phi2 coefficients provided by the summary method for this model are taken from the linear regression theory, and are to be considered asymptoticals.
固定th和阈值变量，该模型是线性的，所以phi1和phi2估计可以直接通过CLS（条件最小二乘）。从线性回归理论PHI1和PHI2summary方法该模型提供的系数的标准误差，并是要考虑asymptoticals。


值----------Value----------

An object of class nlar, subclass setar      
的对象类nlar，子类setar，


（作者）----------Author(s)----------


Antonio, Fabio Di Narzo



参考文献----------References----------




参见----------See Also----------

plot.setar for details on plots produced for this model from the plot generic.
plot.setar图制作这个模型的plot通用的详细信息。


实例----------Examples----------


#fit a SETAR model, with threshold as suggested in Tong(1990, p 377)[适合一个的SETAR模式，与阈值的建议，在通（1990，第377）]
mod.setar <- setar(log10(lynx), m=2, thDelay=1, th=3.25)
mod.setar
summary(mod.setar)

if(require(FinTS)) {
        data(m.unrate)
        setar(diff(m.unrate), ML=c(2,3,4,12), MH=c(2,4,12), th=0.1, include="none")
}

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