How does Random Effects work?
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- Опубліковано 3 жов 2013
- This video explains the mechanism through which Random Effects estimators work, and indicates how it collapses to Pooled OLS and Fixed Effects under certain assumptions.
Check out oxbridge-tutor.co.uk/undergrad... for course materials, and information regarding updates on each of the courses. Check out ben-lambert.com/econometrics-... for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: ben-lambert.com/bayesian/ Accompanying this series, there will be a book: www.amazon.co.uk/gp/product/1... - Навчання та стиль
This video made me understand the relationship between FE, RE and OLS. Awesome video
Thanks, Ben. This series of video has helped me a lot with econometrics. Highly appreciated!
Ben I'm gonna be honest, you're the boss of econometrics
Very good series of panel regression. Ben made the subject very clear! Thank you!
Great video mate, I've been struggling with RE but this makes a lot of sense.
Great content! all questions answered. Thank you
Thank you for making these videos! They are really helpful.
Really helps a lot. Great speaker!
Great explanation!
Incredibly valuable videos, thank you
This is a great explanation, thank you
very explanatory!!! the video is AWESOME !!!
Very clear teaching!
Very straight forward. Thank you
Great explanation thank you!
This guy is an absolute legend
how can we calculate variance of "u" and variance of "alpha", in orher words how can we calculate the "Ui" and "ALPHAit)
Thank you so muh!
How did you got that lambda equation?_?
You can remodel the original equation by taking avgs. of the variables. Then multiply this eqn. by lambda . Then subtract this new equation from the original one.
in the case that RE=FE, why we just assume that T*Sigma(alpha) is equal to infinity and we don't assume that sigma(u) can be equal to zero? thanks
The sigma(u) term indicates the idiosyncratic error, which means that it changes both over time and individual, if this term equals to zero, why we need the control variables? I am not sure whether my answer is right. This is just my consideration.
fantastic
Something that I'd like to add. Sigma square a =0 does not necessarily mean that a is not present.we can delete it. It just means that variance of a across individuals is zero. There is no individual specific effect. In which case we can just use a simple OLS model .a plays no role in analysis.
Great addition. Made it easier to understand.
Who is T?
+Alvaro Moreira t = 1, 2, ...., T
cool
I'd prefer a less mathematical explanation :-/