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I am trying to do a Granger causality test. In the general form Granger causality analysis includes estimating the following equation:

$$y_{t}=a_{1}y_{t-1}+a_{2}y_{t-2}+\dotsc+a_{p}y_{t-p}+b_{1}x_{t-1}+b_{2}x_{t-2}+\dotsc+b_{q}x_{t-q}+e_{t}.$$

It is not necessary that $p=q$, so we can choose different values for $p$ and $q$ (using information criteria such as AIC or BIC). However, EViews forces $p=q$, look here.

Questions:

  1. Is it possible to choose different lag values $p\neq q$ in Eviews?
  2. Doesn't the lack of the ability to choose a lag contradict the theoretical background of Granger causality?
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    $\begingroup$ Only the latter question doesn't it contradict theoretical background of Granger causality is on-topic here. Purely software-focused questions are off topic. $\endgroup$ – Richard Hardy Aug 4 '16 at 7:40
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    $\begingroup$ Before closing as off-topic, consider my first comment: the question in italics is on topic. @Dave, there was not supposed to be any link in my comment. $\endgroup$ – Richard Hardy Aug 4 '16 at 8:19
  • $\begingroup$ I was going through my old answers and noticed this one was not accepted. Do you perhaps need further clarification? $\endgroup$ – Richard Hardy Feb 20 '17 at 19:03
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  1. Off topic (and I do not know the answer).
  2. If the appropriate lags for $y$ and $x$ are different, $p \neq q$, forcing a common lag $r$ will be suboptimal.
    If $r \geqslant \max(p,q)$, there will be unnecessarily many parameters in the model resulting in increased estimation variance and loss of power in the Granger causality test.
    If $r \leqslant \min(p,q)$, there will be omitted variable bias resulting in inconsistent parameter estimates and messing up both the true significance and the power of the Granger causality test.
    If $\min(p,q)<r<\max(p,q)$, there will be a combination of both effects.
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