急：R软件加载程序包tsDyn问题

module(finmetrics)
ndx.ret2 = getReturns(ndx.dat[,"Close"])^2
ndx.rvol = sqrt(aggregate(ndx.ret2, FUN=sum, by="weeks",week.align=1))
ndx.lstar = STAR(log(ndx.rvol), p=2, d=1)
summary(ndx.lstar)

n=length(ndx.rvol)          #302
z=log10(ndx.rvol)[2:(n-1)]  #300
g=ndx.lstar$gamma
th=ndx.lstar$threshold
gfun <- function(y, g, th)      1 / (1 + exp(-g*(y-th)))  
G=gfun(z,g,th)
par(mfrow=c(2,1))
plot(G)

G.zt=as.vector(G)
zt=as.vector(z)
plot(G.zt,zt)

请问前辈有没有三机制或者四机制的LSTAR或者ESTAR的教程和程序吗？有的话麻烦您发给我好吗？谢谢您了！谢谢您最近一直帮助我， 21# epoh

抱歉!我没这方面资料.

你的三机制,四机制,指的可是
multiple regime smooth transition autoregressive

对，就是这方面的资料。 24# epoh

multiple regime smooth transition autoregressive
可以应用package "tsDyn" function star()的引数noRegimes
  function star() can fit with automatic selection
  of the number of regimes based on LM tests.

数据lynx,ndx.rvol都不适合 Add regime
因为 Regime 3 is NOT accepted (p-Value = 0.8678928 )

在此我提供另一个数据svpdx.dat供练习:
The data consist of a time series of daily
  Pound/Dollar exchange rates
  from 01/10/81 to 28/6/85.

library(tsDyn)
svpdx=read.table("svpdx.dat")
mod1.star <- star(svpdx,noRegimes=3, m=2,d=1, control=list(maxit=3000))
mod1.star

Using default threshold variable: thDelay=0
Testing linearity...   p-Value =  0.01685132
The series is nonlinear. Incremental building procedure:
Building a 2 regime STAR.
Using default threshold variable: thDelay=0
Performing grid search for starting values...
Starting values fixed: gamma =  17 , th =  -0.8636345 ; SSE =  467.8267
Optimization algorithm converged
Optimized values fixed for regime 2  : gamma =  17.00000 , th =  -0.9237364
Testing for addition of regime 3.
  Estimating gradient matrix...
  Done. Computing the test statistic...
  Done. Regime 3 is needed (p-Value =  0.004081644 ).
Adding regime  3 .
Fixing good starting values for regime  3 ...
Reordering regimes...
Estimating parameters of regime 3 ...
Optimized values fixed for regime  3 : gamma =  156.3195 , th =  1.685289
Optimization algorithm converged
Optimized linear values:
0.7151267 0.5046379 -0.1193560
-0.7685278 -0.3892954 0.1769597
2.446755 -0.881913 -0.2143251
Ok.
Testing for addition of regime  4 .
  Estimating gradient matrix...
  Computing the test statistic...
Regime  4  is NOT accepted (p-Value =  0.1171196 ).
Finished building a MRSTAR with  3  regimes

> mod1.star
Non linear autoregressive model
Multiple regime STAR model
Regime  1 :
    Linear parameters: 0.7151267, 0.5046379, -0.119356
Regime  2 :
    Linear parameters: -0.7685278, -0.3892954, 0.1769597
    Non-linear parameters:
    22.7263412, -0.9325508
Regime  3 :
    Linear parameters: 2.4467549, -0.8819129, -0.2143251
    Non-linear parameters:
    156.3195267, 1.685289


svpdx.dat
svpdx.rar (8.77 KB) 本附件包括：

• svpdx.dat

2010-10-11 19:27:33 上传

Smooth Transition Autoregressive Models
Smooth Transition Autoregressive Models.pdf (657.91 KB)

2010-10-11 19:27:33 上传

谢谢您啊！ 26# epoh

您好！麻烦您帮我看一下下面的程序，我在R中估计LSTAR模型时，估计的很顺利，但是我想查看中间过程中产生的多有变量结果老是错误：
> sum.s<-summary(ndx.lstar)
错误于summary.lstar(ndx.lstar) : dims [product 48]与对象长度[0]不匹配
> summary(ndx.lstar)
错误于summary.lstar(ndx.lstar) : dims [product 48]与对象长度[0]不匹配
请问您一下这是什么原因呢？希望您给予指导，期待您的回复，谢谢了！



> getwd()
[1] "d:/我的文档"
> svpdx <- read.table("data.txt", header = TRUE);
> svpdx
    cpi  rpi          m2
1   3.4  2.9  0.26524514
2   6.4  5.4  0.31278198
3  14.7 13.2  0.37310154
4  24.1 21.7  0.34529154
5  17.1 14.8  0.29467111
6   8.3  6.1 -0.03867458
7   2.8  0.8  0.34668927
8  -0.8 -2.6  0.17245702
9  -1.4 -3.0  0.14411363
10  0.4 -1.5 -0.31433668
11  0.7 -0.8  0.87572994
12 -0.8 -1.3  0.16337324
13  1.2 -0.1  0.19679602
14  3.9  2.8  0.18161695
15  1.8  0.8  0.14387323
16  1.5  1.0  0.18886459
17  4.8  3.8  0.15787746
18  5.9  5.9  0.18875571
19 -0.7 -1.2  0.18736354
> x <- svpdx$cpi
> x
[1]  3.4  6.4 14.7 24.1 17.1  8.3  2.8 -0.8 -1.4  0.4  0.7 -0.8  1.2  3.9  1.8  1.5  4.8  5.9 -0.7
> y<-svpdx$rpi
> y
[1]  2.9  5.4 13.2 21.7 14.8  6.1  0.8 -2.6 -3.0 -1.5 -0.8 -1.3 -0.1  2.8  0.8  1.0  3.8  5.9 -1.2
> z<-svpdx$m2
> z
[1]  0.26524514  0.31278198  0.37310154  0.34529154  0.29467111 -0.03867458  0.34668927  0.17245702  0.14411363
[10] -0.31433668  0.87572994  0.16337324  0.19679602  0.18161695  0.14387323  0.18886459  0.15787746  0.18875571
[19]  0.18736354
> library(tsDyn)
> ndx.lstar <- lstar(x, m=3,d=1, thVar=z,control=list(maxit=3000));
Using maximum autoregressive order for low regime: mL = 3
Using maximum autoregressive order for high regime: mH = 3
Using only first 16 elements of thVar
Performing grid search for starting values...
Starting values fixed: gamma =  40 , th =  0.3291658 ; SSE =  118.9413
Optimization algorithm converged
Optimized values fixed for regime 2  : gamma =  48.01837 , th =  0.3223614
> rdx.lstar <- lstar(y, m=3,d=1, thVar=z,control=list(maxit=3000));
Using maximum autoregressive order for low regime: mL = 3
Using maximum autoregressive order for high regime: mH = 3
Using only first 16 elements of thVar
Performing grid search for starting values...
Starting values fixed: gamma =  40 , th =  0.352213 ; SSE =  119.8971
Optimization algorithm converged
Optimized values fixed for regime 2  : gamma =  45.87497 , th =  0.3458736
> ndx.lstar
Non linear autoregressive model
LSTAR model
Coefficients:
Low regime:
    phi1.0     phi1.1     phi1.2     phi1.3
-0.2840885  1.9360245 -1.4690397  0.4925477
High regime:
    phi2.0     phi2.1     phi2.2     phi2.3
4.3974268 -2.3388445  2.2482280 -0.9932285
Smoothing parameter: gamma = 48.02
Threshold
Variable: external
Value: 0.3224
> rdx.lstar
Non linear autoregressive model
LSTAR model
Coefficients:
Low regime:
    phi1.0     phi1.1     phi1.2     phi1.3
-0.4204974  1.8394945 -1.4018456  0.4320727
High regime:
   phi2.0    phi2.1    phi2.2    phi2.3
3.939309 -3.911762  3.639241 -1.489407
Smoothing parameter: gamma = 45.87
Threshold
Variable: external
Value: 0.3459
> sum.s<-summary(ndx.lstar)
错误于summary.lstar(ndx.lstar) : dims [product 48]与对象长度[0]不匹配
> summary(ndx.lstar)
错误于summary.lstar(ndx.lstar) : dims [product 48]与对象长度[0]不匹配

26# epoh

这不是数据的问题
因为package自带的数据lynx,也发生同样问题
library(tsDyn)
mod.lstar <- lstar(log10(lynx), m=2, d=1, control=list(maxit=3000))
summary(mod.lstar)
#dims [product 224] do not match the length of object [0]
这是因为source code lstar.R 中的function summary.lstar
sX <- object$mod$thVar
应该是sX <- object$model.specific$thVar
order.L <- object$mod$mL
应该是order.L <- object$model.specific$mL
....
....
我初步修改为lstar_new.R后可运行
但需再花点时间再检查.
source("lstar_new.R")
mod.lstar <- lstar(log10(lynx), m=2, d=1, control=list(maxit=3000))
mod.lstar
summary(mod.lstar)

#####
> summary(mod.lstar)
Non linear autoregressive model

LSTAR model
Coefficients:
Low regime:
    phi1.0     phi1.1     phi1.2
0.4055717  1.2378257 -0.3265076

High regime:
    phi2.0     phi2.1     phi2.2
0.8187712  0.3087342 -0.6418812

Smoothing parameter: gamma = 40

Threshold
Variable: Z(t) = + (1) X(t) + (0) X(t-1)

Value: 2.566

Residuals:
      Min        1Q    Median        3Q       Max
-0.596805 -0.133230  0.034821  0.139065  0.471333

Fit:
residuals variance = 0.04104,  AIC = -348, MAPE = 6.003%

Non-linearity test of full-order LSTAR model against full-order AR model
F = 8.529 ; p-value = 0.00036569

Threshold
Variable: Z(t) = + (1) X(t) + (0) X(t-1)

其实你也可以使用function star
因为star 也是使用Logistic transition function
底下两者结果相同
mod.star <- star(log10(lynx), m=2, d=1, control=list(maxit=3000))
mod.star
summary(mod.star)

mod.lstar <- lstar(log10(lynx), m=2, d=1, control=list(maxit=3000))
mod.lstar

但是summary(mod.star)无误

我修改了source code并重新compile

1.移除原来的package "tsDyn"
2.From menus\Packages\install package(s) from local zip-files\tsDyn_0.7-40.zip


library(tsDyn)
mod.lstar <- lstar(log10(lynx), m=2, d=1, control=list(maxit=3000))
summary(mod.lstar)
#################
Non linear autoregressive model
LSTAR model
Coefficients:
Low regime:
    phi1.0     phi1.1     phi1.2
0.4055717  1.2378257 -0.3265076
High regime:
    phi2.0     phi2.1     phi2.2
0.8187712  0.3087342 -0.6418812
Smoothing parameter: gamma = 40
Threshold
Variable: Z(t) = + (1) X(t) + (0) X(t-1)
Value: 2.566
Residuals:
      Min        1Q    Median        3Q       Max
-0.596805 -0.133230  0.034821  0.139065  0.471333
Fit:
residuals variance = 0.04104,  AIC = -348, MAPE = 6.003%
Non-linearity test of full-order LSTAR model against full-order AR model
F = 8.529 ; p-value = 0.00036569
Threshold
Variable: Z(t) = + (1) X(t) + (0) X(t-1)


tsDyn_0.7-40.zip
tsDyn_0.7-40.zip (1.55 MB)

2010-10-15 22:19:08 上传