Gauss-Markov assumptions part 1

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Күн бұрын

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@evap896 7 жыл бұрын

Dear Ben Lambert, It is now exam season at the University of Birmingham. As a second year who hates economics, you are currently my rock and my hope to pass my Econometrics exam. You are a hero, a true legend, a life saviour. If I pass this year, I will dedicate my dissertation to you and if you're ever in Birmingham, let me buy you food. WRITTEN WITH ALL MY LOVE and several cans of spar budget energy drink

@SpartacanUsuals 7 жыл бұрын

Hi Eva, thanks for your message, and (overly) kind words. Glad to hear that the videos have been useful. Good luck with the spar energy drinks (although perhaps upgrade them to LIDL/Tesco), and your exams and dissertation! Best, Ben

@wypat2717 4 жыл бұрын

you make someone who hates econometric now think that it is actually fun, wish there are more great teaching videos on KZbin like yours. thanks a lot!!

@totochandelier 4 жыл бұрын

just making a pause after some 30 videos... you have a wondurful vision of econometrics, everything is so consistent (...). It is just sometimes a bit complicated for a french guy to understand your Manchester accent...but after some vids it sounds like music of econometrics 😉 thank you so much!

@sauravjha1600 3 жыл бұрын

Thank You so much Ben for your tutorials . These tutorials have motivated me to explore the ideas of econometrics deeply.

@jasonleewkd 10 жыл бұрын

This course is great and a super useful refresher!

@jandanielewicz2 5 жыл бұрын

I like the lehnea parameters part

@mike7920 2 жыл бұрын

Very helpful explanation. Distracting pron of linear

@mousquetaire86 5 ай бұрын

Taleb piqued my curiosity in this topic; coming here for a more meaty explanation.

@lavyniamendonca2021 3 жыл бұрын

so touching for an excellent video

@kejeros 9 жыл бұрын

Why would you make fun of our econometrics lord and savior

@ppthar 9 жыл бұрын

Just found ur channel which i find pretty useful :D Thnx a lot Ben ^^

@codyfindlay3653 10 жыл бұрын

Lin-e-ar

@TheMabax 9 жыл бұрын

Thank you so much for this course..

@Me-ji2pn 9 жыл бұрын

2:44 I thought that if there is a multiple that the equation is still linear?

@NhatLinhNguyen82 9 жыл бұрын

+sltr1 It think you are right since a product of 2 constants is just another constant. Thus, the parameter is still linear

@wahabfiles6260 4 жыл бұрын

@@NhatLinhNguyen82 how is variable different then parameters? It said variables can be non-linear

@xiao6322 9 жыл бұрын

Thank you. Very useful for me to prepare my quant interview. Reading takes more time

@kottelkannim4919 4 жыл бұрын

Following the outline of Gauss-Markov theorem proof kzbin.info/www/bejne/r3m7m42Kis-tnac I noticed that one suggests an alternative linear estimator. This alternative linear estimator is then shown to have larger variance of the LS estimator. This suggested alternative estimator is not only an estimator of a model which is linear in parameters (parameters==LS estimators), but also linear in the dependent variable observations or can be made to be linear in these observations. In the hope I am not misleading any reader, each of the linearity constraints seem to imply the other .

@guilhermefreire8093 2 жыл бұрын

If a function is non linear in parameters like y = alpha + beta squared times x + u, couldnt i simply substitute beta squared with another variable say, gamma, and we would get y = alpha + gamma x + u? Wouldn't it be linear then?

@msam6789 4 жыл бұрын

Plz do videos on estimability and identifiability

@kottelkannim4919 4 жыл бұрын

Following the outline of Gauss-Markov theorem proof kzbin.info/www/bejne/r3m7m42Kis-tnac I noticed that one suggests an alternative linear estimator. This alternative linear estimator is then shown to have larger variance of the LS estimator. This suggested alternative estimator is not only an estimator of a model which is linear in parameters (parameters==LS estimators), but also Linear in the dependent variable. So linearity of BLUE estimators has both constraints: A. The model is linear in the estimators. B. The estimators are linear in the dependent variable observations.