Thank you very much, Mike.
Rashmi
Quoting "Michael S. Hanson" <[email protected]>:
> 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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