Multicollinearity
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- Опубликовано: 22 авг 2024
- This video explains what the issue of multicollinear regressors causes for estimation, using the example of TV and Radio advertising. Check out ben-lambert.co... for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: ben-lambert.co... Accompanying this series, there will be a book: www.amazon.co....
oh man you should be my professor you know what watching your videos helps a lot more than my professor's lectures....
Ben is Da Bomb - made it from 1-60 videos so far, actually quite enjoy studying econometrics now xD Cheers Ben!
This video is crazily good! Never understood econometrics better, and it's actually making fun to study it! :)
Saving me right now with online classes. Thank you!
me too
Thank you! its so helpful, the explanation is easy to be understand.
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You have genius teaching skill.
Excellent presentation. I'm watching your videos to better understand the quant section of CFA Level II. Thank you Ben!
Hi, thanks for your message, and kind words. Best of luck with the CFA! Hope it all goes well. Cheers, Ben
9 years later I am doing the same thing. Hope they were helpful for you. Thanks Ben!
Youre Videos are great short but well explained !
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You're awesome Ben! Very helpful videos
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Thank you doctor for the presentation especially exemplification
Very well explained and demonstrated. Many thanks.
Thanks for this explanation. So my understanding is that multicollinearity is only worth finding out if you want to know how much each attribute is contributing to the model. Which, if you want to be prudent, you should find out. So how would you find out? Run a regression twice where with one of the attributes held out in the first regression and the other held out in the second regression? Then compare the two results to determine which one has more effect on the sales? Thank you in advance.
you're a king mate
Brilliant, thank you!
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Hi Ben. I am trying to watch the videos based on the order in the playlist. But you've not talked about R2 and significance level yet and now are using these concepts!
Hi, thanks for your suggestion. I realise there are some conflicts here and there. I will add a link to these topics in the video. Best, Ben
Very useful!! Thank you so much
Dr. Lambert, I really enjoy your videos. I have two continuous variables: rcs(Age, 5) and rcs(GRE_score, 6) that I relaxed the cubic splines on and now I a getting huge VIF values for each of those variables. Does VIF work with variables that have relaxed cubic splines please? Thank you for your important work.
I think if you try to Reinfeld equation against the null thatll help. Wald's theory of VIF works with variables that have relaxed cubic splines. Remember: your GE number might be low when using the SRI technique on VIF. Good luck!
Thanks... I still don't understand..damn i am so weak in this math thingy..
Hi Ben, which software do you use for these illustrations?
thank you very good
So thank you.
Hi, Ben, it's really helpful.
But I was wondering if we need to check the multicollinearity for variables like dummies and time trend. Because I suppose for example dummies for structural break should be highly correlated to some variables and that is the point of using them, right? and same for time effect.
+CH H Hi, thanks for your comment. Yes, it is possible for multicollinearity to occur with dummies and time trends. Imagine that you have time series data on the sales of ice creams. In the summer there will be higher sales than the winter. You could either model this using a variable like temperature, or indirectly using a dummy which is 1 when it is summer, and 0 otherwise. These variables will be highly collinear, because they are both attempting to measure the same thing. Intuitively regression is going to find it hard to differentiate between the effects of the dummy vs the temperature variable, and hence collinearity may be a problem here. Hope that helps! Bests, Ben
Thanks for answering my previous question. I was wondering if you could answer my question which is related to multicollinearity. The question gives you 4 auxillary regressions. One of them is... logX1 (t ratios) 0.96 (2.56) -0.83logX2 (3.49) 0.95logX3 (5.66) 0.6logX4 (3.79). I presume the parentheses are standard errors. But how do you perform an f test on that to confirm multicollinearity (related to previous part of the question).? Your help would be greatly appreciated!
Hi Ben your explanations are really good. Do you have any videos on multilevel or hierarchial modelling explaining the math of it?
Can someone explain why the standard errors for the B-coefficients are getting bigger because of the multicollinearity?
have a look at the formula derived for standard error of estimates of coefficients in whatever book you are using. You'll find that it has a term involving correlation of the independent variables. A high correlation, that is Multicollinearity, hence inflates the standard error
Why does this occur only in regression problems and not in classification ?
thanks man
why do you think when we make centering for our variables the collinearity disappear?
Hi, thanks for your message. Making variables standardised can reduce correlation between the two estimators in question. However, it does not remove collinearity in general between two variables. Hope that helps! Best, Ben
This is good
will the estimates of Beta 1 and Beta 2 be unbiased or biased?
Thanks for the great video.
wrg, can conclux any nmw
American English is the worst. I love British form. Atleast you are able to understand what a person is saying
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hey dude why u tryina act lik khan man ur not like khan, khan is 10x better
Your mama is so fat that she has a condition number over 9000!!!!
@nyecompilations you should probably go to khan than troll here and criticize for no reason while you yourself have nothing productive to contribute.