Thanks for your great video! But one question regarding your explanation: I don't think the only potential term equaling zero is Exp(Error(t-1)^2), instead it should be one Error(t-i) within k
I must say, you must have had at least one (if not more) crazy (extraordinary) professors :-) during your graduate program-one of whom you referred to in your previous lecture. Otherwise, how could someone learn such challenging concepts so thoroughly? Kudos to your professors, your dedication to learning, and your excellent explanations! :-) i would request you to make more vedios on Bayesain stats, sampling biases and Methods of Moments, if you have time. ... Thank you
If the MA model uses the errors from the previous periods to forecast, why are we not using the PACF (which is the correlation of residual over the actual values) to determine the appropriate q for the MA model?
6:54 in the Auto-correlation term: Why you aren't taking in consideration in the second term E(Xt)*E(Xt-k) ?! Shouldn't it be auto-correlation is diffrent from 0 if the first term is diffrent from μ^2 ?
@@massivefoot Yeah I think you are right, he actually forgot to mention that the left side which is E(Xt * Xt-k) always yealds µ² + something, and he prouved when that something is equal to 0. So the µ² from the left side and the one from the right always cancel out.
That's true but that's not what the statement's saying (note that it's NOT equal to 0). The statement says that the only way to get (at least) two terms in common is for k less than or equal to q.
@@utpalpodder-pk6vq I think you are right. The condition in the video is wrong. He was trying to show there is no overlap, and if there is no overlap there will be no common term present, and if there are no common term present, it should be = 0 and not != 0. So if it is = 0 (no overlap), then the last term E[X(t-q)] should not overlap the first term E[X(t-k)]. Hence, t-q should be >= t-k instead.
hi there - can you give any guidance on the method used to fit MA(q) processes - i.e. find the phi parameters. I can't find much information about this
Great video but from around 6:30 onwards your words do not match the equations you write. You are saying in words that the inequality between t-q and t-k leads to the overall expected value being zero when it actually leads to the overall expected value being NON-zero as in the equals sign with a cross through it on the left. Took me a while to figure out what you were saying.
To further clarify, if the very last term in X_t with time interval t-q is SMALLER than the t-k value of the very first term in X_t-k, then there is identical error variable overlaps and hence an overall non-zero expected value. On paper what you write makes complete sense but your words say the opposite.
Oh, it's using the entire covariance equation: E[xy] - E[x]E[y]. The mu^2 gets cancelled out.
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@@scottpease9827 Hi, I was wondering the same .. do I understand it correctly that: E[xy] = mu^2 (if the errors are not overlapping) and E[x]E[y] = mu^2, then: E[xy]-E[x]E[y]=mu^2-mu^2=0?
Thx for the video ritvikmath, i have one question ( i might missed it in the video). Video tells us why ma(3) dont use et-4 term bcs its autocorr is 0 thus it wont add anything to our model. Just like AR vs PACF logic. is that correct? So this is like an explanation video for "why we dont use all the lags and cut the formula after k=q?" I hope i made myself clear. Have a great day.
Why expectation (Xt,Xt-k) is not zero for most terms as except first term all other terms have error terms? As you said expectation of error will always be equal to zero.
A basic question comes to mind I.e if the expected value of error term is zero, then why at all include the error terms in the time series prediction . In case the EV of Error is not zero, then can the EV value be straight away added to time series prediction without doing all the correlation calculations
Hi great videos..really helping a lot..i am just starting data science and economics course...can u please help by making videos on basiscs like wold decomposition, invertibility, impulse response,Linear fiters and forecasting
While plotting graph of ACF or PACF against lag ...you talked about error band ....so what is the range of error band we should take.... I mean what are the parameters of error band we should consider..... please reply fast
Such a masterpiece! You're still saving a lot of helpless students like me, even after a few years!
Great way of teaching the intuition behind the equation.
Keep up the good work.
Thanks for your great video! But one question regarding your explanation: I don't think the only potential term equaling zero is Exp(Error(t-1)^2), instead it should be one Error(t-i) within k
wonderful, I was looking all over the internet for a decent explanation, thanks
Glad you liked it!
I like your videos but it would be helpful if you had a link in the description to your other videos referenced in your talk.
great suggestion!
Yes, order your videos in a playlist
Your videos are great! Really thank you, from Brazil! ❤
You're wonderful. Please keep the videos coming!
instablaster
I must say, you must have had at least one (if not more) crazy (extraordinary) professors :-) during your graduate program-one of whom you referred to in your previous lecture. Otherwise, how could someone learn such challenging concepts so thoroughly? Kudos to your professors, your dedication to learning, and your excellent explanations! :-) i would request you to make more vedios on Bayesain stats, sampling biases and Methods of Moments, if you have time. ... Thank you
Thank you so very much Ritvik.
You're most welcome
what is overlap, i dont understand why t-q
Awesome content!
excellent videos! so easy to understand
super helpful! Thanks so much for your ecxcellent work!
Glad it was helpful!
Great way of teaching! Thank You
You're very welcome!
Thanks a lot for your clear explanation!
If the MA model uses the errors from the previous periods to forecast, why are we not using the PACF (which is the correlation of residual over the actual values) to determine the appropriate q for the MA model?
6:54 in the Auto-correlation term: Why you aren't taking in consideration in the second term E(Xt)*E(Xt-k) ?!
Shouldn't it be auto-correlation is diffrent from 0 if the first term is diffrent from μ^2 ?
He's dropped the mu terms. But if you expand everything out (and use the fact that E[epsilon] = 0) then the mus all cancel out.
@@massivefoot Yeah I think you are right, he actually forgot to mention that the left side which is E(Xt * Xt-k) always yealds µ² + something, and he prouved when that something is equal to 0. So the µ² from the left side and the one from the right always cancel out.
Thanks gentleman for your video.
Thanks for watching!
shouldn't the equation at 6:39 be other way around ? as if we don't want any term in common we need t-q to be greater then t-k!
That's true but that's not what the statement's saying (note that it's NOT equal to 0). The statement says that the only way to get (at least) two terms in common is for k less than or equal to q.
thanks for helping !
@@roryokane341 i think there is some mistake in the condition....for the condition (t-q)
@@roryokane341 i think the statement is wrong if k
@@utpalpodder-pk6vq I think you are right. The condition in the video is wrong. He was trying to show there is no overlap, and if there is no overlap there will be no common term present, and if there are no common term present, it should be = 0 and not != 0.
So if it is = 0 (no overlap), then the last term E[X(t-q)] should not overlap the first term E[X(t-k)]. Hence, t-q should be >= t-k instead.
great explanation in simple terms
hi there - can you give any guidance on the method used to fit MA(q) processes - i.e. find the phi parameters. I can't find much information about this
Excellent sir.
Great video but from around 6:30 onwards your words do not match the equations you write. You are saying in words that the inequality between t-q and t-k leads to the overall expected value being zero when it actually leads to the overall expected value being NON-zero as in the equals sign with a cross through it on the left. Took me a while to figure out what you were saying.
To further clarify, if the very last term in X_t with time interval t-q is SMALLER than the t-k value of the very first term in X_t-k, then there is identical error variable overlaps and hence an overall non-zero expected value. On paper what you write makes complete sense but your words say the opposite.
@@alexbenfield8690 Yes, it should be strictly t-q is GREATER than t-k, rightly pointed.
thank you so much , it helps me a lots
You're welcome!
What about the mu's in the E[x(t)*x(t-k)] part? I don't understand why there isn't a mu^2 somewhere?
Oh, it's using the entire covariance equation: E[xy] - E[x]E[y]. The mu^2 gets cancelled out.
@@scottpease9827 Hi, I was wondering the same .. do I understand it correctly that:
E[xy] = mu^2 (if the errors are not overlapping) and E[x]E[y] = mu^2, then:
E[xy]-E[x]E[y]=mu^2-mu^2=0?
Thx for the video ritvikmath, i have one question ( i might missed it in the video). Video tells us why ma(3) dont use et-4 term bcs its autocorr is 0 thus it wont add anything to our model. Just like AR vs PACF logic. is that correct? So this is like an explanation video for "why we dont use all the lags and cut the formula after k=q?" I hope i made myself clear. Have a great day.
Shouldn't k
thank you so so much
Super Like
Thank you!
Why expectation (Xt,Xt-k) is not zero for most terms as except first term all other terms have error terms? As you said expectation of error will always be equal to zero.
A basic question comes to mind I.e if the expected value of error term is zero, then why at all include the error terms in the time series prediction . In case the EV of Error is not zero, then can the EV value be straight away added to time series prediction without doing all the correlation calculations
Same doubt
what if there is no E t-1 but we have E t-2. Will it be Moving Average 1 or 2?
Hi great videos..really helping a lot..i am just starting data science and economics course...can u please help by making videos on basiscs like wold decomposition, invertibility, impulse response,Linear fiters and forecasting
Thank you 🙏
pretty neat
Glad you think so!
So what is the value of q?
i love you
While plotting graph of ACF or PACF against lag ...you talked about error band ....so what is the range of error band we should take.... I mean what are the parameters of error band we should consider..... please reply fast
Thanks for the video. Can anyone please explain what is the expectation value?
The formula you're using for ACF is incorrect. That's autocovariance and not autocorrelation.