Could you provide an example of how to run Recentred Influence functions in Python to analyze unconditional partial effects on quantiles within a regression analysis framework (i.e., unconditional quantile regressions)?
My question is why is it necessary though? The prediction from an OLS give you the conditional mean of the normal distribution, we also already have a constant variance, from there we can then calculate any quantile we need.
Good question. If your distribution is known or assumed (e.g. Normal) you are right. OLS doesn't have to assume the distribution. If you have an unknown distribution, the first 2 moments won't give you the quantiles. Also, you might want to fit the quantiles directly because you don't trust the normality assumption.
Super helpful explanation Meerkat, thank you!
Could you provide an example of how to run Recentred Influence functions in Python to analyze unconditional partial effects on quantiles within a regression analysis framework (i.e., unconditional quantile regressions)?
Thank you sir
My question is why is it necessary though? The prediction from an OLS give you the conditional mean of the normal distribution, we also already have a constant variance, from there we can then calculate any quantile we need.
Good question. If your distribution is known or assumed (e.g. Normal) you are right. OLS doesn't have to assume the distribution. If you have an unknown distribution, the first 2 moments won't give you the quantiles. Also, you might want to fit the quantiles directly because you don't trust the normality assumption.
@@MeerkatStatistics Fantastic!
🙏🙏🙏 THAX