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I have a static panel data model with small T (T=5) that makes it impossible for me to use granger causality as it requires a long time span.

So my question:

  • Is there any alternative solution to test for causation even in a small T context?

Any hint will be highely appreciated!

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  • $\begingroup$ what's the other dimension of the panel (i.e. N)? $\endgroup$
    – user603
    Sep 19, 2010 at 14:41
  • $\begingroup$ thanks Jeromy for your reply,my second dimension is N=16. $\endgroup$
    – Ama
    Sep 19, 2010 at 16:27
  • $\begingroup$ Sorry i mean Kwak $\endgroup$
    – Ama
    Sep 19, 2010 at 16:36
  • $\begingroup$ Ama>: imho that's much too little. Even if the observations were drawn from a single process (i.e. a single run of 4*16=64 differentiated observations), the granger test for causality would probably require more data points. Now in your case you would also have to account for the loss of degrees of freedom to control for the fact that your observations are coming from different process. $\endgroup$
    – user603
    Sep 19, 2010 at 16:52
  • $\begingroup$ sure, you are right, but is there any possibility to bypasse this issue?? $\endgroup$
    – Ama
    Sep 19, 2010 at 16:59

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I guess the current (econometrics) industry standard for this setting is fixed effects regression. Take a look at the section on panel data in this paper by Austin Nichols for a concise discussion. For these kinds of analyses you want larger N, typically, though. (By the way, for those with a background in statistics rather than econometrics, the usage of "fixed effects" is different for econometricians. For them, it means a regression that uses differencing or dummy variables to account for unmeasured (linearly additive) confounders in a repeat observation setting. This is different than what statisticians usually mean, which is usually in contrasting "fixed" and "random" effects.)

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