Why do I get the same results when I do OLS using statsmodels and PooledOLS using scikit?

Question

I'm currently doing a machine learning project using python (beginner here, learning everything from scratch).

Just wanna know what's the difference between statsmodels' OLS and scikit's PooledOlS using the same panel dataset I have. I tried both and they gave me the same results. Does that mean they're essentially doing the same thing but from different packages? Am I supposed to get the same results? Or is it that I am doing something wrongly?

My dataset looks something like that below:

                  excessreturnlag1m      ROA  ...  momentum6m  momentum12m
bank  date                                    ...                         
bankA 2019-06-30         -14.564600   0.9795  ...        0.14        -0.24
      2019-05-31           7.522300   0.9795  ...       -0.69        -1.97
      2019-04-30          -2.020400   0.9795  ...        1.36        -1.70
bankB 2019-06-30          -5.969600   0.9915  ...       -0.39        -1.77
      2019-05-31           0.220200   0.9915  ...       -0.24        -2.00
      2019-04-30          -1.900000   0.9915  ...       -0.06        -1.42
bankC 2019-06-30           2.721700   0.9763  ...       -0.38        -1.13
      2019-05-31          -8.418900   0.9763  ...       -1.28        -1.19
      2019-04-30          -1.001100   0.9763  ...       -3.06        -1.16

I currently have a MultiIndex (bank and date) in my Dataframe. Am I supposed to use that to do a panel regression?

Edit: OK from what I understand, PooledOLS is a "special" case of multiple linear regression so it will give the same results as statsmodels' OLS? Correct me if i'm wrong!

Answer 1

OLS : Ordinary Least Squares is just a simple calculation method .
The implementation may differ depending on how to solve the normal equation , but it may be GOOD to measure the execution time because there are differences in the amount of calculation.