方老师：
        在“协整向量检验案例分析的问题”一课中，我想到一个问题，请帮忙解答，谢谢。
原程序：

  uscn.f = lexrates.dat[,"USCNF"]##提取远期汇率
uscn.s = lexrates.dat[,"USCNS"]##提取近期汇率
uscn.ts=seriesMerge(uscn.s,uscn.f)

# determine lag length using AIC
var.fit = VAR(uscn.ts,max.ar=6,criterion="AIC")#构建VARP模型，最大 的阶是6阶，
var.fit$ar.order#提取最合适的阶是2，那么，VECM模型最大的阶是1.
coint.rc = coint(uscn.ts,trend="rc",lags=1)#提取最合适的阶是2，那么，VECM模型最大的阶是1.
#也就是lags=1，trend="rc，表示是用约束常数项的模型，即第二种模型
结果：
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> summary(coint.rc)
Call:
coint(Y = uscn.ts, lags = 1, trend = "rc")
Trend Specification:
H1*(r): Restricted constant
Trace tests significant at the 5% level are flagged by ' +'.
Trace tests significant at the 1% level are flagged by '++'.
Max Eigenvalue tests significant at the 5% level are flagged by ' *'.
Max Eigenvalue tests significant at the 1% level are flagged by '**'.
Tests for Cointegration Rank:
         Eigenvalue Trace Stat  95% CV  99% CV Max Stat  95% CV  99% CV
H(0)++**  0.0970    32.4687    19.9600 24.6000 24.8012  15.6700 20.2000
H(1)      0.0311     7.6675     9.2400 12.9700  7.6675   9.2400 12.9700

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据此，我们判断uscn和uscf存在协整关系。

如果我们用：

##########
coint.nc = coint(uscn.ts,trend="nc",lags=1)
summary(coint.nc)
##
----------------------------------------------------------------------------------------
> summary(coint.nc)
Call:
coint(Y = uscn.ts, lags = 1, trend = "nc")
Trend Specification:
H2(r): No constant or trend
Trace tests significant at the 5% level are flagged by ' +'.
Trace tests significant at the 1% level are flagged by '++'.
Max Eigenvalue tests significant at the 5% level are flagged by ' *'.
Max Eigenvalue tests significant at the 1% level are flagged by '**'.
Tests for Cointegration Rank:
         Eigenvalue Trace Stat  95% CV  99% CV Max Stat  95% CV  99% CV
H(0)++**  0.0680    17.6212    12.5300 16.3100 17.1061  11.4400 15.6900
H(1)      0.0021     0.5150     3.8400  6.5100  0.5150   3.8400  6.5100
Unnormalized Cointegrating Vectors:
也说明存在协整关系
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问题：用nc模型检验也通过，用rc模型也通过。为什么不选nc模型而用rc模型呢？以后碰到类似的问题应该怎么选、选哪个模型做VECM呢？