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Hello:
Somebody can help me with this test?
The User's guide explains in details what this test does when I open some series as a group (page 376) but what does in VAR or VEC?
Thanks

The same thing, but you don't need to provide the number of lags for the test in the VAR object, since the VAR object already has a number of lags specified.

Thank you for your reply.
Although I think they are diferent because each test uses diferent statistics: F when I open the data as a group and Chi-sq when I work with a VAR.

Moderator Garrett says they should be the same, but I have not manage to get the same results -- that is, from the Granger test given under Lag Structure for a VAR test, and the Pairwise Granger test ust for the Group of the same two variables. I'm using the identical first-difference variables, with the same number of observations for both tests. There is also a constant term for both (as a default in the Group test). (I am not talking here about the difference between the Chi-Square and the F-test statistics, which I think should be identical in this case.) Thanks, JS

I am wondering how you estimate the optimal lag length.

Fot the VEC Granger test you estimate the optimal lag length with a VAR model at level and for a VAR Grangertest you estimate the optimal lag length with a VAR model at first differences. Is that correct?

Moderator Garrett says they should be the same, but I have not manage to get the same results -- that is, from the Granger test given under Lag Structure for a VAR test, and the Pairwise Granger test ust for the Group of the same two variables. I'm using the identical first-difference variables, with the same number of observations for both tests. There is also a constant term for both (as a default in the Group test). (I am not talking here about the difference between the Chi-Square and the F-test statistics, which I think should be identical in this case.) Thanks, JS

Could you provide an example?

Sure, here's the example. First, the two first-differenced series (don't worry, they're short):

obs D(TURN02) D(EMPCONS)
1990 NA NA
1991 NA NA
1992 NA -0.0601278679999994
1993 NA -0.0448243470000004
1994 NA 0.0201069809999996
1995 -0.0806199999999996 0.0214456070000004
1996 -0.1015800000000000 -0.0628500140000003
1997 -0.0645199999999999 -0.0668865179999995
1998 0.0552200000000002 -0.0084477390000002
1999 -0.0483700000000002 -0.0053353019999998
2000 -0.0955800000000000 0.0257837109999998
2001 -0.0683600000000002 0.0131298119999998
2002 -0.0399799999999999 -0.0119123229999997
2003 -0.0888799999999996 -0.0242382860000001
2004 0.0288500000000002 -0.0015276730000000
2005 -0.0689100000000006 0.0170763609999999
2006 -0.0320499999999999 0.0343223999999997
2007 -0.1189999999999990 0.0310736980000001
2008 0.0334599999999998 -0.0007252490000003

(Note that first 5 years on 1st series, and first 2 on the 2nd series are blank.)

Next, I now give both Granger printouts, 1st on the Group:

(1) Pairwise Granger Causality Tests
Date: 09/06/12 Time: 12:15
Sample: 1990 2008
Lags: 2

Null Hypothesis: Obs F-Statistic Prob.

D(TURN02) does not Granger Cause D(EMPCONS) 12 1.54698 0.2777
D(EMPCONS) does not Granger Cause D(TURN02) 3.01391 0.1137

... and 2nd from the VAR:

(2) VAR Granger Causality/Block Exogeneity Wald Tests
Date: 09/06/12 Time: 12:18
Sample: 1990 2008
Included observations: 12


Dependent variable: D(TURN02)

Excluded Chi-sq df Prob.

D(EMPCONS) 6.027810 2 0.0491

All 6.027810 2 0.0491


Dependent variable: D(EMPCONS)

Excluded Chi-sq df Prob.

D(TURN02) 3.093967 2 0.2129

All 3.093967 2 0.2129

Looks the same to me.

Thank you, my mistake. I guess I didn't expect the significance levels for the two tests to be so different.

In regard to the earlier question about optimal lag length, i should say that i don't know the answer.

This thread is easy to google but I still need a bit more explanation concerning this problem, it would be great if you could help me.

I am interested in the granger causality between wage and profits and I use gdp as a control variable.

The pairwise granger causality test gives me:



So Granger causality seems to run from profits to wage, correct? That gdp is causing wage is intuitive enough. Additionally I did the VAR Granger causality test:



and I have trouble comparing the two. My VAR test statistics aren't just the pairwise test statistics times 2. Do you know why?

Thanks a lot for your answers,
Filiuspublii