Відео

I love proofs like this where there is a “aha” moment where it all becomes clear. Thanks !

Please make a video on endogeneity. I have watched several video but there is no good content available. I believe you may explain that very well.

he makes so many mistakes in these videos its terrible

Welcher wwu Krieger ist hier?

this is the best econometrics video ever!!!!!

it feels like the homoskedasticity refers to MSE but not variance as it measures the error which is the difference between the predicted value and the acual value. what does xi represent here?

Excellent explanation; thank you, Ben.

Thank

this is 100 time better than my prof

amazing video

Really nice video i don't understand why you name epsilon as itta they are different letters

Great refresher for me, thanks

Hi Mr Lambert, may i please request that you cover the lewbel IV method. thanks

Thanks Ben. Good vid

This video is crazily good! Never understood econometrics better, and it's actually making fun to study it! :)

Thank you so much!

du you have a video on this for k coeficciencts? as in matrice notation: y = Xβ +e

High curvature -> sharp -> concentrated -> low variance. Makes sense.

cut out the 'sort of' 🤣such a brit!

Sir can u make one video of the construction of reference priors by taking example of one standard distributions

Great! You let me understand the concept confusing me for a long time! Thank you!

Very helpful, thank you!

since beta is negative as exponent, ln-transformed version should be "minus beta" times price P?

brilliant explanation, very intuitive, thanks Ben!

Does every independent variable take away the supposed-to-be omitted variable from the error term equally?

Great Explanation!

Amazing video !

The video doesn't explain the n-c elements to be removed.

5:44 why - 0.5?

Hmmm. The energy required to do the walk is an incorrect analogy. A wide low plateau has the same area (integral) as a low wide plateau, but the wide low plateau would be easier to walk across. Admittedly, this was only an analogy, and the integrals at 5:28 are the rigorous versions of the statement.

Thanks for the video!

Ben, you are an absolute legend. Thanks so much for these videos!!

Thankyou for the great explanation.

So to make sure; one can say that log of the odds is equivalent to the dot product (wT dot X) which is where we get our linear combination?

you are a legend thank you

Excellent Ben! Thank you!

If the value of F-statistics is larger, there is a higher probability of rejecting the null hypothesis. That means there is a higher possibility of a significant correlation between Y (result e.g. Sales) and X (variable on which dependency is being studied e.g Advertisement on TV)

Thank you for the video! Could you please explain why the random effect estimator is biased?

Could you please explain why the random effect estimator is biased? Thank you!

Best explanation I have ever heard!

Man I have an exam tomorrow. Am I cooked?

thank u man

I finished my Econ undergraduate course 2 years ago, and forgot everything I learnt. I worked in a completely different field from economics for two years(audit), and honestly I felt it a pity that I didnt make an effort to retain that knowledge, so thank you for the free lectures.

Ben you are saving my graduation!!! Hello from Brazil

Good video. Another source of endogeneity due to omitted variables is parent's iQ. If iQ affects (not deterministically) how much education someone obtains and affects wages, but is omitted from the regression, then it is included in the error and we have OVB. Parent's iQ may affect their chosen education level. Parent's iQ is transmitted to children because of genetics. So therefore, if iQ is initially omitted and causes endogeneity, using parent's education level (also affected by iQ) is an endogenous instrument because it is still omitted if it is related to child's iQ (through genetics).

So with the Dickey-Fuller test, we are basically testing for the stationarity of the series ONLY in terms of the variance, right? Because if there is a constant term (i.e., alpha =/= 0), then regardless if rho is equal to 1 (as in the null hypothesis) or less than 1, the series is still non-stationary in general because, with a constant term, the series will have a trend and the expected value of the series is not constant at zero. Is this correct?

I wonder what is the first difference of the time dependent dummy variable i.e delta dt in the above equation?

Thank you so much, Ben! This was very helpful.

Very good, thank you the typo could Be a bit better ....😂

how do you run a t-est in a multivariate regression? Wouldn't B_2hat (LS) be B1+B2 here? I'm missing something