Excellent videos.I really don't know how to thank you.I am a masters student of Health economics and your videos really taught me the reason behind a lot of things. Thanks a lot. I will try to watch all the 200 videos:)
It would cause your estimators to be biased unless of course B0 would have been 0 in the simple linear regression. The reason being is that the equation for y = B0 + B1*x + u. That u (it’s mu technically) is there to account for all the other variables that we haven’t taken into account. One part of a technical definition for unbiased would be that E(u) = 0. But in thinking about it, it would be weird if the expected value of all other variables was 0. Instead, if E(u) equals some value a, we subtract that from u and put it on B0 so now B0 would be a. Thus making E(u-a) = 0 yet keeps the equation the same since you did y = (B0 + a) + B1*x + (u-a). Now back to the assumption of B0 = 0, if it happens to be that E(u) != 0, we’ll run into problems as B0 won’t be able to be changed to make E(u) = 0. This would leave the estimator biased as a core assumption of linear regressions is that E(u) = 0
In general I like your videos. But in my opinion you go to quickly through the difficult staff (i.e. derivations) and you concentrate too much on easy stuff (for example unbiasdness)
I'm a big fan of your videos, BUT for the next 3 videos, which are a bit of a chore to work through, it would be great if you could explain *why* it is necessary to learn the derivation of the OLS estimator. Thank you Ben! :)
hey man thanks for saying what "linear" means...watched the whole video wanting you to explain it and you completely swept it under the rug and wasted 7 minutes of my life
Thanks for sacrificing your time to teach the world. I like your videos
Excellent videos.I really don't know how to thank you.I am a masters student of Health economics and your videos really taught me the reason behind a lot of things. Thanks a lot. I will try to watch all the 200 videos:)
May your estimators be blue!
thank you good sir youre a legend. much better than my professor.
Buddy, great job but 2:22 is so confusing...
watch the previous videos on bias and consistency of estimators
Thanks Ben. Good vid
thank you
Thank you so much for the videos.
Can you also suggest me a book I could follow up along the videos?
God bless you. I am soo grateful
Thank you so much! From textbook to visuals, much easier!
How do you know if estimator is consistent? If n increases, and it approaches X, it is consistent. But who can do that in practice?
It means you have a larger piece of the pie to do calculations and thus more data what leads to Bhat approaching Bx
Thank you man
@Ben Lambert - if we assume that ß0 = 0 (false assumption), will this change our other estimators to be biased?
It would cause your estimators to be biased unless of course B0 would have been 0 in the simple linear regression.
The reason being is that the equation for y = B0 + B1*x + u. That u (it’s mu technically) is there to account for all the other variables that we haven’t taken into account.
One part of a technical definition for unbiased would be that E(u) = 0. But in thinking about it, it would be weird if the expected value of all other variables was 0. Instead, if E(u) equals some value a, we subtract that from u and put it on B0 so now B0 would be a. Thus making E(u-a) = 0 yet keeps the equation the same since you did y = (B0 + a) + B1*x + (u-a).
Now back to the assumption of B0 = 0, if it happens to be that E(u) != 0, we’ll run into problems as B0 won’t be able to be changed to make E(u) = 0. This would leave the estimator biased as a core assumption of linear regressions is that E(u) = 0
In general I like your videos. But in my opinion you go to quickly through the difficult staff (i.e. derivations) and you concentrate too much on easy stuff (for example unbiasdness)
I love u marta
Thank you Ben
Thank you Ben !!!
Every time I learn these topics, I pray all the very best for you. Thank you so much! No one explains econometrics as well as you do.
I'm a big fan of your videos, BUT for the next 3 videos, which are a bit of a chore to work through, it would be great if you could explain *why* it is necessary to learn the derivation of the OLS estimator. Thank you Ben! :)
hey man thanks for saying what "linear" means...watched the whole video wanting you to explain it and you completely swept it under the rug and wasted 7 minutes of my life
He explained it in a previous video, but it’s also pretty easy to just google...
why so mean?