# st: granger causality and OLS

 From <[email protected]> To [email protected] Subject st: granger causality and OLS Date Fri, 21 Nov 2003 14:27:25 -0800 (PST)

```I am having some troubles understanding the command
gcause.

Suppose I have the following dataset:

X               Y
77.76959	87
80	        87
60	        87
60
60	        86
60	        86.99999
70
70	        92
70

If I run OLS, I get the following output:

. reg  X L.X  L.Y

Source |       SS       df       MS
Number of obs =       6
-------------+------------------------------
F(  2,     3) =    0.28
Model |  51.8576108     2  25.9288054
Prob > F      =  0.7760
Residual |  281.475659     3  93.8252197
R-squared     =  0.1556
-------------+------------------------------
Total |   333.33327     5   66.666654
Root MSE      =  9.6863

------------------------------------------------------------------------------
X            |      Coef.   Std. Err.      t    P>|t|
[95% Conf. Interval]
-------------+----------------------------------------------------------------
X            |
L1 |   .2991619   .4668727     0.64   0.567
-1.186636    1.784959
Y            |
L1 |   .6536429   2.134428     0.31   0.779
-6.139059    7.446345
_cons        |  -11.07649   186.8041    -0.06   0.956
-605.5707    583.4177
------------------------------------------------------------------------------

If I run gcause, I get the following output:

. gcause X Y, lags(1) reg
Granger causality test                         Sample:
2002m11 to 2003m3

obs = 3
H0: Y does not Granger-cause X

F( 1, 1) =    0.00
Prob > F =   1.0000

chi2(1) =    0.00      (asymptotic)
Prob > chi2 =  1.0000      (asymptotic)

Source |       SS       df       MS
Number of obs =       3
-------------+------------------------------
F(  1,     1) =    0.20
Model |  44.6704698     1  44.6704698
Prob > F      =  0.7316
Residual |  221.996184     1  221.996184
R-squared     =  0.1675
-------------+------------------------------
Total |  266.666654     2  133.333327
Root MSE      =    14.9

------------------------------------------------------------------------------
X            |      Coef.   Std. Err.      t    P>|t|
[95% Conf. Interval]
-------------+----------------------------------------------------------------
X            |
L1 |   .4312049   .9612718     0.45   0.732
-11.78291    12.64532
Y            |
L1 |  (dropped)
_cons        |   35.36556   70.30683     0.50   0.703
-857.9674    928.6985
------------------------------------------------------------------------------

My questions are:

1.  Why does gcause use fewer observations (3 instead
of the 6 available)?

2.  Why does it allow to test granger causality if Y
is dropped in the regression?

Thank you.

Raffaella Baldi

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