Great appreciation to this awesome Copula series videos! I have one question if I may: at 9:03 here you create marginal uniform distribution by ranking and normalization. Well, if your earlier video "Copulas 4 - creating simulation with copulas", you assume marginal distribution to Normal or T-Dist, then you use inverse CDF to obtain uniform distribution. So what is the fundamental difference of these two ways that you showed in these two videos. Thank you very much in advance for your advice!
thanks so much for these videos. Would it be possible to get the slides used in the videos after the intro, and also the example dataset showed in this video please?
The data's a bit tricky, but I'm planning on sharing the slides on LinkedIn over the coming weeks - you can find me there at linkedin.com/in/profpaulsweeting
Hi! The only reason I used columns 2 to 4 was to show how you can fit a copula to a subset of the data. If you wanted to use just (say) the first 4 columns of 6, it would be retsum_u[,1:4], whereas if you had only 4 columns and you wanted to use them all, it would just be retsum_u (i.e. no brackets needed)
How do I compare different Archimedian copula models to see which one is better fit if I am using "itau" or "irho" method as there is no Liklihood function?
I'm not sure it's easy in R, as I fitcopula only calculates the likelihood if the approach used is maximum likelihood. You can see this because using (e.g.) the AIC function on the copula object throws up an "NA". You could calculate it directly from the copula density function, but these are quite unwieldy for Archimedean copulas. So probably easiest to just use the maximum likelihood estimation approach if you want to compare likelihoods...
Great appreciation to this awesome Copula series videos! I have one question if I may: at 9:03 here you create marginal uniform distribution by ranking and normalization. Well, if your earlier video "Copulas 4 - creating simulation with copulas", you assume marginal distribution to Normal or T-Dist, then you use inverse CDF to obtain uniform distribution. So what is the fundamental difference of these two ways that you showed in these two videos. Thank you very much in advance for your advice!
thanks so much for these videos. Would it be possible to get the slides used in the videos after the intro, and also the example dataset showed in this video please?
The data's a bit tricky, but I'm planning on sharing the slides on LinkedIn over the coming weeks - you can find me there at linkedin.com/in/profpaulsweeting
Sir how to add covariates to these models?
why are we only using the columns 2 to 4? and which columns do i use when i have 4 columns in total? (min 14)
Hi! The only reason I used columns 2 to 4 was to show how you can fit a copula to a subset of the data. If you wanted to use just (say) the first 4 columns of 6, it would be retsum_u[,1:4], whereas if you had only 4 columns and you wanted to use them all, it would just be retsum_u (i.e. no brackets needed)
Thanks for the video @@PaulSweeting
Is there any way to "check" if our fit is correct by using this colum 1 ?
How do I compare different Archimedian copula models to see which one is better fit if I am using "itau" or "irho" method as there is no Liklihood function?
I'm not sure it's easy in R, as I fitcopula only calculates the likelihood if the approach used is maximum likelihood. You can see this because using (e.g.) the AIC function on the copula object throws up an "NA". You could calculate it directly from the copula density function, but these are quite unwieldy for Archimedean copulas. So probably easiest to just use the maximum likelihood estimation approach if you want to compare likelihoods...