我在用R包中自带的数据进行门限回归学习；发现有些问题要向请教。

> library(TSA)

> data(prey.eq)

> prey.tar.1=tar(y=log(prey.eq),p1=4,p2=4,d=3,a=.1,b=.9,print=TRUE)

time series included in this analysis is: log(prey.eq)

SETAR(2, 1 , 4 ) model delay = 3

estimated threshold = 4.661 from a Minimum AIC fit with thresholds

searched from the 17 percentile to the 81 percentile of all data.

The estimated threshold is the 56.6 percentile of

all data.

lower regime:

Residual Standard Error=0.2341

R-Square=0.9978

F-statistic (df=2, 28)=6355.76

p-value=0

Estimate Std.Err t-value Pr(>|t|)

intercept-log(prey.eq) 0.2621 0.3156 0.8305 0.4133

lag1-log(prey.eq) 1.0175 0.0704 14.4455 0.0000

(unbiased) RMS

0.05479

with no of data falling in the regime being

log(prey.eq) 30

(max. likelihood) RMS for each series (denominator=sample size in the regime)

log(prey.eq) 0.05114

upper regime:

Residual Standard Error=0.2676

R-Square=0.9971

F-statistic (df=5, 18)=1253.556

p-value=0

Estimate Std.Err t-value Pr(>|t|)

intercept-log(prey.eq) 4.1986 1.2841 3.2697 0.0043

lag1-log(prey.eq) 0.7081 0.2023 3.5005 0.0026

lag2-log(prey.eq) -0.3009 0.3118 -0.9648 0.3474

lag3-log(prey.eq) 0.2788 0.4063 0.6861 0.5014

lag4-log(prey.eq) -0.6113 0.2726 -2.2427 0.0377

(unbiased) RMS

0.07158

with no of data falling in the regime being

23

(max. likelihood) RMS for each series (denominator=sample size in the regime)

0.05602

Nominal AIC is 10.92

我的问题是：

在高区中的滞后2，3项的回归系数在一定的显著水平下，P值不算好。那么我肯定要在滞后2，3中进行选择最好的。比如，我首先选择lag3-log的系数为零，进行回归，再来看结果。。。。。。

请问：

1、在门限回归中要不要对各区的系数进行显著性或不显著性判定（用其P值）。

2、如果应当这样作的话，那么我们又如何来限制不显著的系数为零呢？

请就这个结果和prey.tar.1=tar(y=log(prey.eq),p1=4,p2=4,d=3,a=.1,b=.9,print=TRUE)语句邦助修改。谢谢！

3、另外我试了一种限制的办法，也是不行的:

>prey.tar.1=tar(y=log(prey.eq),p1=4,p2=4,

+fixed=c（NA,Na,0,Na,NA),d=3,a=.1,b=.9,print=TRUE)

错误于tar(y = log(prey.eq), p1 = 4, p2 = 4, fixed = c(NA,Na,0,Na,NA), :

参数((fixed = c(NA,Na,0,Na,NA))) 没有用。

请版主邦忙解决（如何处理不显著的系数）。