Message from Nikephoros in Vibrant Diversity #general
Do you have a TA that's of any use?
Do you know anyone who's already taken the class and could help?
TAs are usually useless anyway
From what I understand, It's very straightforward if you know what these outputs mean
Like does Prob > F = (#) mean Fail to reject or reject H0
Which I haven't done since python 2.7 came out
I know a guy who's premed who could help but I'm not sure I'll be able to get a hold of him
I have a friend at Case Western, I can shoot him a text and ask if he knows
So none of those outputs mean anything to you?
Lol not at the moment
Sorry, but I'll ask a couple of friends and see if they can help
ok the simplest way you can ask them is this: "Do these results indicate that I can reject H0?"
You know what, I think the decision rule is Reject H0 if "Prob > F" is greater than "F(#, #)"
Which appears to be true
He's a bit of an autiste so he takes a while to get his thoughts out
I see. I'm fairly certain that I can reject H0. Even if I don't know how to explain it using the data, the underlying question is: Is "egg" the cause of "Chicken"
The answer is, presumably, yes.
@Nikephoros I wish I could offer help. I used STATA for econometrics as well, but that was nearly 10 years ago.
I did my econometrics project on whether baseball player salaries are representative of objective output.
Spoiler alert, they aren't unless you time shift the pay by like 2 years IIRC.
@Koba I really don't know why its formatted "Prob > F", I think it might refer to a critical value of some sort.
Because I read that as "probability of f = .8903"
"Prob of chi2 = .8877"
There is nothing special about granger causality versus other hypothesis tests in stata.
So what should I interpret these outputs to mean?
Reject H0, right?
So, I read your results as failure to reject
Ah so I was reading it backwards
Yes, I agree with that. I think you have to accept H0 with those numbers because it doesn't meet the confidence interval.