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ar5iv.org/pdf/2203.11820

Presently I don't have this data however most of the data is from RBI and World Bank official websites. Thank you. Do subscribe.

I can tell you about IIT roorkee. No there is not. The Time slots will be different for Men and Women.

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​​@@RishiMusic001No,dear IIT staff member nd family members only.

It is linear interpolation.

To get the exact weight multiply the eigen values with the rotted component vector. Then sum the resultant value the absolute value ignoring - sings is the absolute weight..

You are welcome. Thanks

Thank you for your comment. I understand your concerns about taking the logarithm of a negative number, which is indeed mathematically undefined. However, in practice, a common method to address this issue is to add a constant to all values to ensure they are positive before taking the log. This technique, known as log transformation with a shift, is often used in research to stabilize variance and normalize data. It helps in better handling skewed distributions and can be particularly useful in time series analysis and other statistical methods. While it changes the absolute values, it preserves the relative differences, which is often what matters most in analysis. I hope this clarifies the rationale behind the procedure.

just as an update on this, I've just did it with eViews coding lw=@recode(w>0,log(1+w),-log(1-w)) and I've got the same result. so yes, this method works ! thank you again!!

Take the exponential of the series.

Thank for your comments. Well for different variables we have to take maximum minimum value from that particular variable.. as rage of variable will be different.

@@NirmalSingh-yy4lc And for each year, Maximum and Minimum Value are also going to be different... Right??

@@soumikchoudhury4852 not for each year but will be same for the period under consideration.

@@NirmalSingh-yy4lc please perform the above analysis on stata as well

In that case you can take log directly no need to adopt any additional procedure.

Using samne proceedure you can create weight index.. First normalise the data for dimensions of financial inclusion index..

You get the rotated factor value multiple this with the eigen values... Then sum the resultant value the absolute value ignoring - sings is the absolute weight..

Hello.. First you decide the dimensions and variables. Then after normalisation of data is required then you can apply this proceedure.

👍

This is the criterion which you can us to assign weights to different dimensions of an index to form composite index.

How to know weights of dimensions

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Welcome

Thank you so much ma'am 🙏🏼

You are welcome.

Welcome

I'm not sure, but I think that procedure will also not change the relative difference hence that can also be applied but in the situation you are talking the current procedure can be applied.

Thank you so much

Thank you.

Thank you

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😁😁😁

You might have complete data on banking and non banking companies for other variables hence dropping companies does not seem a good idea you can consider dropping the variable.

@@NirmalSingh-yy4lc yes sir, and thanking for reply Is there any solution also there🙏

@@ramsharma8843 welcome, you can look for alternative proxies for interest coverage ratio. Explore the literature in this regard.

@@NirmalSingh-yy4lc thanku sir, doing it

You need to have some data for that particular crosssectional unit which you want to fill because as this metod interpolate। Using the crosssectional unit. In this case bank code.

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