How best can I use the Granger causality test in time series data and understand it better because I have never used it. I want to analyze long run relationship and bi-directional relationship between two variables.
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$\begingroup$ @Andy edited in the granger-causality tag, which shows that there are lots of questions on the topic on this site. Have you looked at any of them? Are you sure you have a new question? Have you studied basic texts and internet sources? $\endgroup$– Nick CoxCommented Jun 7, 2014 at 10:10
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$\begingroup$ from the few sources I have looked at, the model seems to be written different and the are barely describing the letters on the model $\endgroup$– DanielCommented Jun 7, 2014 at 10:20
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$\begingroup$ I don't think I understand that comment. However, you seem to be asking for a personal tutorial starting from scratch on what in its field is an utterly standard topic. You may be lucky, but a common attitude from many people here will be "Please do read a textbook at your level". $\endgroup$– Nick CoxCommented Jun 7, 2014 at 10:24
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$\begingroup$ will try to get to books thou I just wanted a little understanding especially on interpreting the symbols on the model $\endgroup$– DanielCommented Jun 7, 2014 at 10:34
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$\begingroup$ Daniel, why don't you try writing out the model (using the notation from the texts) and ask specific questions about the symbols you are puzzled by? $\endgroup$– AlexisCommented Jun 7, 2014 at 16:07
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1 Answer
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Here is an off the cuff answer.
Granger causality means that the shock or error from one variable has a cross correlation or impacts another.
H0 or the null hypothesis is NO CAUSALITY. It means your result falls within a range or confidence interval close to zero significance.
If the p>.05, the probability you 'struck out' and the one variable does not impact the other is high enough you 'cannot reject the null of no causality.' Stats-speak: you must accept that you failed to show causality on the variable.
