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st: granger causality and OLS


From   <raffaella_00@yahoo.com>
To   statalist@hsphsun2.harvard.edu
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
-------------+------------------------------          
Adj R-squared = -0.4074
       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
-------------+------------------------------          
Adj R-squared = -0.6650
       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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