STATA: plotting and interpreting quantile on quantile for Non-Stationary Variables

#Estimating quantile on quantile using simple #quantile regression may lead to invalid estimates in the presence of #non-stationary variables. This video augments the #quantile-on-quantile model by using the #Quantile ARDL model rather than Quantile regression to generate the #autocorrelation robust long-run and short-run estimates. This video can also help in making a quantile-on-quantile model with #control variables which is known as a #partial quantile-on-quantile model. The visualization of the adjustment #coefficient also helps in assessing when the overall model will be effective.
The codes are available here: econistics.com...
Title: STATA: plotting and interpreting quantile on quantile for Non-Stationary Variables
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[@MyQamarali]

Fabulous Job Sir

[@nomanarshed]

Thanks

[@jayagupta9239]

sir, i want to ask whether the variables in the first difference are stationary i.e., they are integrated of order 1 or not and whether we can simply imply first difference variables in stationary in the first video that you share
please help

[@nomanarshed]

If the variables are integrated to order 1 then their first difference forms are stationary variables.

[@muhammedashiqv1109]

Here also link is not working to get code to practice

[@nomanarshed]

You can get them using ruclips.net/video/7tCTaO2hGgY/видео.html the website is under development