I have a rather complicated model that I am testing on a panel data set, containing 3 categorical variables and 3 continuous ones, where I want to specifically test for the interaction effects between the categorical and continuous variables. So far, I have used, in Stata, fixed effect and random effect regression, both with and without the -robust- option. Unfortunately, I have been getting no or very few significant coefficients as results. The question I have is whether tinkering with the regression type, e.g. using -xtmixed- or other regression models, will
- has a good chance of achieving significant results. More generally, does using different, I guess more "complex", models, often result in a change of significance of effects?
- is there a decent statistical justification for this? My knowledge of the topic is rather less comprehensive, and the changes to the model I have undertaken so far feel more like random tinkering than anything substantial. I'd be happy if by chance significant effects show up, but I feel like I would have a hard time justifying the whats and whys.
Thanks so much for your consideration!
All the best, Daniel