Hi everyone!!!
My name is Anna,I'm pretty new to this forum so please excuse if I'm using any of the options wrong!
I have a question concerning my bachelor thesis.
I'm doing a panel regression with a dataset I made myself. After implementing the breuschpagan and the hausman test I thought I was good to go doing a Fixed Effects Method (as the hausman test showed me to do so) and I already finished all the robustness checks when I realised that one of my variables is timeinvarient and therefor not included in my model.
Now I am thinking of doing a random effects model instead which would make more sense in the theoretical sense as the timeinvarient variable that is excluded in the model is quite important. But on the other hand it would make less sense in the methodological way as RE is biased by time-constant unobserved heterogeneity – Since time-constant unobserved heterogeneity is ubiquitous in non-experimental social research, RE estimates generally will be biased.
What do you guys think is the better idea?
For some context: I am doing my research on the impact of parties (partisan theory) on the share of renewable energies in germany by federal state for the years 2002 to 2020.
My timeinvariant variable therefor is a meteorological variable concerning the wind speed per federal state which is - safe to say - quite important considering the fact that wind energy is one of the biggest renewable energy sources in germany. so neglecting that would be quite a big thing.
So should I do a random effects analysis instead which would that the variation across entities is assumed to be random and uncorrelated with the predictor or independent variables included in the model which theoretically doesn't make much sense.
Another option is also doing a fixed effects model and also a crosssectional data regression.
I would be very glad if someone of you guys could help me and give me some advice!
Thanks and have a lovely weekend!
My name is Anna,I'm pretty new to this forum so please excuse if I'm using any of the options wrong!
I have a question concerning my bachelor thesis.
I'm doing a panel regression with a dataset I made myself. After implementing the breuschpagan and the hausman test I thought I was good to go doing a Fixed Effects Method (as the hausman test showed me to do so) and I already finished all the robustness checks when I realised that one of my variables is timeinvarient and therefor not included in my model.
Now I am thinking of doing a random effects model instead which would make more sense in the theoretical sense as the timeinvarient variable that is excluded in the model is quite important. But on the other hand it would make less sense in the methodological way as RE is biased by time-constant unobserved heterogeneity – Since time-constant unobserved heterogeneity is ubiquitous in non-experimental social research, RE estimates generally will be biased.
What do you guys think is the better idea?
For some context: I am doing my research on the impact of parties (partisan theory) on the share of renewable energies in germany by federal state for the years 2002 to 2020.
My timeinvariant variable therefor is a meteorological variable concerning the wind speed per federal state which is - safe to say - quite important considering the fact that wind energy is one of the biggest renewable energy sources in germany. so neglecting that would be quite a big thing.
So should I do a random effects analysis instead which would that the variation across entities is assumed to be random and uncorrelated with the predictor or independent variables included in the model which theoretically doesn't make much sense.
Another option is also doing a fixed effects model and also a crosssectional data regression.
I would be very glad if someone of you guys could help me and give me some advice!
Thanks and have a lovely weekend!

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