thanks for your teaching and i have a little question. when i check the stationarity in an AR(p) model, Is same result obtained regardless of delta(constant term in model) ??
Uhm this might be a stupid question, but I do not understand the factorisation of the characteristic equation. You are replacing teta for phi ánd factorizing in the same step. Can you further explain?
For me he speaks a bit too slow, so I think it depends on the person. It would be best if he kept his pace as he does. We can adjust it ourselves by putting the playback speed a bit up or down. You can do this when you click on the gear wheel in the bottom of the video. About the larger font, I would agree with you. His writing is luckily enough very neatly, though.
![ARMA Stationarity, Invertibility, and Causality [Time Series]](/img/1.gif)
thanks for your teaching and i have a little question. when i check the stationarity in an AR(p) model, Is same result obtained regardless of delta(constant term in model) ??
Uhm this might be a stupid question, but I do not understand the factorisation of the characteristic equation. You are replacing teta for phi ánd factorizing in the same step. Can you further explain?
Hey! Did you figure it out?
@@sidddddddddddddd the factorization is wrong
how do you get p= 2?
because this is quadratic equaion, so you have at most 2 non-identical roots
What if the roots of the characteristic equation are imaginary?
3:15 bookmark(21.5.1.)
It is really very fine and explanatory. Pl. use large fonts. Speak slowly as we read and understand both together. Dr. Jha
For me he speaks a bit too slow, so I think it depends on the person. It would be best if he kept his pace as he does. We can adjust it ourselves by putting the playback speed a bit up or down. You can do this when you click on the gear wheel in the bottom of the video.
About the larger font, I would agree with you. His writing is luckily enough very neatly, though.