8. Time Series Analysis I

MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Peter Kempthorne This is the first of three lectures introducing the topic of time series analysis, describing stochastic processes by applying regression and stationarity models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

Comments

1. the order of the lecture is indeed questionable. Should talk about ar and ma first and then go to arma and arima.
2. Bad presentation.
3. time series segment starts at 26:46
4. My analysis after watching this for 30 minutes: It is clear that the person behind the camera has no idea what the person in front is talking about and does quite some weird switching back and forth between slides and presenter.
5. those symbols are killing me
6. Hank Paulson?!?!??!?!
7. He knows his stuff, but he is not relaying that information very well. His method of teaching is not effective. He is all over the place and his thoughts are not organized in a way for people to follow through. I am afraid that he is making the subject of statistics boring. Statistics should be fun and more engaging. and what is up with the slides? he sounds like a consultant...the more you confuse people the more you make money :) ...
8. Some of the things he explains are not very clear, I don't understand how you lose degrees of freedom when p tends to infinite. Can someone explain this? (I refer to point 40:00)
9. Really useful for my a basic understanding of time series, have to do my thesis on this and I didn't even realise it was a stocastic process...
10. I believe there is an error on the slide with AR(1) model. The variance of X sub t should be sigma squared over one minus phi squared. Phi is not squared in the slides.
11. He's speaking another language to me.  What he's saying clearly has meaning to those people that can interpret it and he clearly has passion and knows what he's saying with supreme command.  He's obviously spent a long time and a lot of trial and error to get to the level he's at.  I doubt my brain will ever achieve such a high level of understanding.
12. This dude is making this far more complicated than required.  I just sat my exam on this gear.  It is not this complicated.  Put a single R output up there, and realise it is just a couple of numbers and a little playing.  The math behind it is tricky, but this is not really that hard.....certainly not as hard as he is saying.
13. 9:42....normal linear regression has that the sum of e^ = X^2......This is co-efficient hypothesis stuff.  I thought this still operated under GM where the errors summed to 0
14. Qualitative material in the form of PowerPoint presentation is just plain lazy and hard for students who doesn't know this material, very hard to follow. I would expect something more from an institution such as MIT
15. I don't think it is nice to post quantitative stuff all in power point. I don't know what he is talking about when camera cast on instructor not on the power point.
16. what are the prerequisite for this class? I found myself very hard to understand what he is talking about, given I already learned basic statistics, e.g. random variable, central limit theorem, normal distribution...
17. thank you for this video,
    I'd like to know what could be the quantitative criteria to rank time series by a decision maker, I suppose that each time series represents an alternative.
    
    Thanks