I am currently using statsmodels (although I would also be happy to use Scikit) to create a linear regression. On this particular model I am finding that when adding more than one factor to the model, the OLS algorithm spits out wild coefficients. These coefficients are both extremely high and low, which seems to optimise the algorithm by averaging out. It results in all of the factors being statistically insignificant. I am just wondering if there is a way that I can put an upper or lower limit on the coefficients such that the OLS has to optimize within these new boundaries? Continue reading...
