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Thanks for all the videos, they are quite helpful. Unrelated: After watching to a lot of these videos I can say that I've never heard any English-speaker vocalizing the "intrusive r" pronunciation as much and as intensively as Ben Lambert! ( e.g. Beta(i) is pronounced as "bitter eye", idea as "I dear" etc)

شكرا

I am a little confused. People tell me that Bayesian statistics is great because you don't have to choose an arbitrary cut-off value for the p-value. But in this case you still have to decide if you are interested in an 80 %, 95 % etc. credible interval.

What happened to alpha ?

a sufficient explanation

Great

Why are we multiplying them in this very specific manner tho ?

Beautifully explained

10/10 explanation thanks so much

Thanks. How do you know what E is?

Hi, I wanted to know if I have multiple endogenous variables, then do the IVs I use to estimate each of them, do they have to be the exact same? Is it okay if I use the same IVs but for different years?

Thank you very much for the tutorial, what subjects in statistic and math do you recommend to refresh before I start econometrics.

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?