Unbiasedness of OLS - part two

[@ReneeMaliziaMarandu]

You are literally saving my life. im writing my econometrics test tomorrow and i really didn understand anything .. going through ur videos bit by bit just putting things together. HOWEVER i must say ur videos are a bit scattered i dnw where to begin first n what video follows next wish you numbered them so it can be lyk a syllabus i can follow. Thanks so much anyways xx

[@cca211]

Hi Ben, do you know which video was it when you explained how (xi-xbar)xi = (xi-xbar)(xi-xbar)? I've followed your videos, but I can't recall the explanation for this. Thank you!!

[@indragesink]

cca211 I think it is vid. 16.

[@Kavafy]

@@indragesink I still can't see it!

[@1982sadaf]

Sxx also is random, isn't it? Each time we draw a sample and make an beta_hat estimate, that is based on a sample of Xs too. So Sxx is also a random variable, that has expectation. hm... Then how you brought it out?

[@ChuanziYue]

+1982sadaf However according to random selection, one of Gauss Markov hypothesis, Xs are not correlated with one another thus one can prove the rest.

[@moobadaa]

Hi Ben. What is the difference between this video and video number 63? in both cases you've just proved that our OLS estimator is unbiased! Is it just a repetition? Thanks

[@SpartacanUsuals]

Hi, the videos are two (slightly) different ways of showing this. I admit there is a bit of repetition there. Thanks for your feedback. Best, Ben

[@kizitodavinci3152]

@@SpartacanUsuals please leave this video as it is although it is a repetition

[@trouserpython3857]

The acoustics in the room this is being recorded in lead the Trouser Python to believe Benny Lambert recorded this while on the toilet.

[@leerobinson1866]

Is your name 'trouser python' because you're a dick? Your comment suggests that you are

[@manyadeshpande3329]

I’m a little confused at 2.41 Shouldn’t the entire term become NB^P? Because of the existence of the summation. How does it just result into B^P?

[@mezz8587]

Hi Ben, can you please tell me in which video did you explain how to prove that the a estimator is unbiased? Thank you!