[R] What is the CADF test criterion="BIC" report?

[Pfaff, Bernhard Dr.]

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 [mailto:r-help-bounces at r-project.org] Im Auftrag von 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


> data(Canada)

> ADFt <- CADFtest(Canada[,1], max.lag.y = 14, criterion="BIC")

> summary(ADFt)
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)

> summary(ADFt1)
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

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