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Re: st: help on granger causality


From   Rashmi Shankar <rashmi@brandeis.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: help on granger causality
Date   Tue, 28 Jun 2005 13:02:53 -0400

Thank you very much, Mike.


Rashmi


Quoting "Michael S. Hanson" <mshanson@mac.com>:

> On Jun 27, 2005, at 4:56 PM, Rashmi Shankar wrote:
>
> > Hi, all: After running a var in first differences, 4 lags, I use
> > vargranger to
> > run a pair-wise causality test. The output is as follows. How do I
> > interpret
> > the causality test result?
> >
> > . var  bp_level lnpetrol lndomcred if cnum==1,lags(1/4)
>
> 	[output deleted]
>
> > . vargranger
> >
> >     Granger causality Wald tests
> >    +------------------------------------------------------------------+
> >    |          Equation           Excluded |   chi2     df Prob > chi2 |
> >    |--------------------------------------+---------------------------|
> >    |          bp_level           lnpetrol |  14.108     4    0.007    |
> >    |          bp_level          lndomcred |  6.0917     4    0.192    |
> >    |          bp_level                ALL |  19.279     8    0.013    |
> >    |--------------------------------------+---------------------------|
> >    |          lnpetrol           bp_level |  5.9199     4    0.205    |
> >    |          lnpetrol          lndomcred |  5.4121     4    0.248    |
> >    |          lnpetrol                ALL |  10.121     8    0.257    |
> >    |--------------------------------------+---------------------------|
> >    |         lndomcred           bp_level |   49.78     4    0.000    |
> >    |         lndomcred           lnpetrol |  7.3881     4    0.117    |
> >    |         lndomcred                ALL |  57.215     8    0.000    |
> >    +------------------------------------------------------------------+
>
> 	"Granger causality" tests -- or more correctly perhaps, Granger
> non-causality tests -- are statistical tests of "causality" in the
> sense of determining whether lagged observations of another variable
> have incremental forecasting power when added to a univariate
> autoregressive representation of a variable.
>
> 	The test itself is just an F-test (or, as above, a chi-squared test)
> of the joint significance of the other variable(s) in a regression that
> includes lags of the dependent variable.  For example: in your above
> results, at traditional levels of significance, one would reject the
> null hypothesis that 'lnpetrol' does not "Granger cause" 'bp_level'.
> On the other hand, at traditional significance levels, one would reject
> Granger causality of either 'bp_level' or 'lndomcred' for 'lnpetrol'.
> That is, neither of these variables appear to have incremental
> forecasting power for 'lnpetrol' once one conditions on 4 of its own
> lags.
>
> 	It is very important to understand what Granger causality is _not_.
> First, it cannot establish causality in a theoretical sense.  In a
> classic example, a rooster may "Granger cause" the sunrise.  Second,
> Granger causality tests may be misleading if, for example, the
> processes determining the variables of interest involve expectations.
> Third, Granger causality is not a test for strict exogeneity.  For
> these issues and additional critiques of the (mis-)use of Granger
> causality, consult any of the textbooks mentioned in the [TS] entry for
> -vargranger-, such as Luetkepohl (1993), pp. 35-43, Hamilton (1994),
> pp. 302-309, or Enders (2004), pp. 283-287 and 357-358.
>
>                                          -- Mike
>
> *
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>


-- 
Rashmi Shankar
Asst. Professor, Department of Economics,
Brandeis International Business School
Brandeis University,
415 South Street,
Waltham, MA 02454
Phone: 781-736-2265
Fax: 781-736-2269
*
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