# GLS models: how to interpret results and how to run predictions [closed]

I have data that is quite heteroscedastic, and therefore decided to try fitting a GLS model in python with the statsmodels package in python.

The data has two continuous feature variables with skewed distributions with a continuous response variable. The data is NOT time series. I did not know how to specify "sigma" in the model, so I just left it as "None".

The model performed well, with an r2 value of ~ 98%! However I am completly clueless as how else to evaluate the model, particularly in comparison to my earlier OLS and polynomial regression models.

I tried to compute the Mean Average Error, by running a batch of predictions and finding their mean difference with the actuals. But this approach yielded a pretty large deviation.

How else can I evaluate the model? Am I running predictions correctly?

I have never worked with GLS or WLS models before, and the math goes a bit over my head. Any tips on how to help? Thanks!

• $R^2$ of 98% is often too good to be true.... Commented Mar 29, 2020 at 12:09
• Mean Average Error has no obvious virtues if the fitting criterion is any flavour of Least Squares. There are few details here, but a key point that can be made is that GLS or WLS doesn't imply a different functional form, just different ways of handling errors. I think you need to cross-reference your previous thread all over again and explain that you can't post any data. stats.stackexchange.com/questions/455049/… I fear that this is too broad if the answer sought is general or too vague if you want insights for your current project. Commented Mar 29, 2020 at 12:16
• @NickCox So would it be helpful to post my data? I am trying to avoid having to go into too much detail. Secondly, is Mean Average Error not particularly useful then? Why? My end goal is to use the model to make predictions real time. Commented Mar 30, 2020 at 13:11
• I've been urging all along that you post data. You are minimising a sum of squares criterion, so some kind of root mean square error seems far more pertinent. Otherwise you are judging baseball by the criteria of tennis. Commented Mar 30, 2020 at 13:16
• Okay, would you recommend I repost? Do you want to see a table of the data? Or data that's charted? What exactly would be helpful? Commented Mar 30, 2020 at 13:19