Balancing via Logistic Regression
It sounds to me like regression models are not the right approach here. If I understand correctly and you want to find companies which are comparable based on different financial ratios you should use clustering algorithm like k-means or just just use a distance metric like euclidean distance to find the closest neighbor for each company.
I have a question about logistic regression. For my thesis I have to compare companies using different financial ratios using Python. The financial ratios should be balanced either by logistic regression or probit regression. I have already looked at some tutorials and these have worked with explanatory and dependent variables, but there are no dependent variables in my data set. I only need the regression to balance the different financial ratios and not to explain dependent variables. The goal is to calculate a score ("Propensity Score") for each company, from which I can then calculate the distance to the "nearest" company and thus get a proxy for comparability with other companies.
Unfortunately, I completely lack the approach here and I can't find any instructions or examples. Does anyone of you have an idea how I can proceed here? I hope my problem becomes clear.