Hello Paul,
just a guess: different sample sizes! In your first call, the sample
is shorter than in your second. Hence, you can test this, if you
curtail your data set in your second call and then you should obtain
the same result, i.e.:
library(vars)
data(Canada)
test <- summary(CADFtest(Canada[-c(1:13), 1], max.lag.y = 1))
test
Augmented DF test
ADF test
t-test statistic: -1.389086
p-value: 0.855681
Max lag of the diff. dependent variable: 1.000000
Call:
dynlm(formula = formula(model), start = obs.1, end = obs.T)
Residuals:
Min 1Q Median 3Q Max
-0.79726 -0.20587 -0.03332 0.23840 0.70460
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.471789 17.521147 1.397 0.167
trnd 0.009959 0.006941 1.435 0.156
L(y, 1) -0.026068 0.018767 -1.389 0.856
L(d(y), 1) 0.615983 0.092632 6.650 7.18e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3533 on 65 degrees of freedom
Multiple R-squared: 0.413, Adjusted R-squared: 0.3859
F-statistic: NA on NA and NA DF, p-value: NA
Though, I am not the package maintainer who could provide you with
more insights, but the source code itself.
Best,
Bernhard
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Von: r-help-bounces at r-project.org
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p99323005 at ntu.edu.tw
Gesendet: Montag, 14. November 2011 04:35
An: r-help at r-project.org
Betreff: [R] What is the CADF test criterion="BIC" report?
Hello:
I am a rookie in using R. When I used the unit root test in
"CADFtest", I got the different t-test statistics between using
criterion="BIC" and no using criterion. But when I checked the
result with eviews, I find out that no using criterion is correct.
Why after using criterion="BIC", I got the different result?
Paul
ADFt <- CADFtest(Canada[,1], max.lag.y = 14, criterion="BIC")
Augmented DF test
ADF test
t-test statistic: -1.389086
p-value: 0.855681
Max lag of the diff. dependent variable: 1.000000
Call:
dynlm(formula = formula(model), start = obs.1, end = obs.T)
Residuals:
Min 1Q Median 3Q Max
-0.79726 -0.20587 -0.03332 0.23840 0.70460
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.342321 17.435476 1.396 0.167
trnd 0.009959 0.006941 1.435 0.156
L(y, 1) -0.026068 0.018767 -1.389 0.856
L(d(y), 1) 0.615983 0.092632 6.650 7.18e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3533 on 65 degrees of freedom
Multiple R-squared: 0.413, Adjusted R-squared: 0.3859
F-statistic: NA on NA and NA DF, p-value: NA
ADFt1 <- CADFtest(Canada[,1], max.lag.y =1)
Augmented DF test
ADF test
t-test statistic: -2.7285715
p-value: 0.2282588
Max lag of the diff. dependent variable: 1.0000000
Call:
dynlm(formula = formula(model), start = obs.1, end = obs.T)
Residuals:
Min 1Q Median 3Q Max
-0.84769 -0.24745 -0.02081 0.24187 0.82344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.661910 17.439021 2.733 0.00776 **
trnd 0.019217 0.007005 2.743 0.00754 **
L(y, 1) -0.051256 0.018785 -2.729 0.22826
L(d(y), 1) 0.753011 0.075724 9.944 1.61e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3937 on 78 degrees of freedom
Multiple R-squared: 0.5674, Adjusted R-squared: 0.5508
F-statistic: NA on NA and NA DF, p-value: NA