Hello everyone,
For my thesis, I am looking at the effect of environmental controversies on the profitability of Chinese and European firms. I am using panel data (2013-2018).
As I want to compare both groups (China vs. Europe), I split the dataset and ran 2 regressions.
After computing the Hausman test for the European dataset, it showed that random effects model is the superior model. Not problems here.
However, for the Chinese dataset it would be beter to use a fixed effects model (according to Hausman).
With panel variable id (company) and time (year) (strongly balanced), i conducted the following fixed effects model:
xtreg ROA EnvCont1_lag EnvCont2_lag CEP1_lag CEP2_lag GUOState OwnCon RD FirmSize Leverage i.Industry i.Year, fe robust
(OwnCon, RD, FirmSize, Leverage and Industry are my control variables)
I included i.Industry and i.Year as I would like to add industry and time fixed effects as well. I think this is the correct way?
However, when I run the regression, the variables OwnCon (time-invariant) and i.Industry are omitted (because of collinearity), and there is barely any significance.
Also, GUOState (dummy for state ownership) is omitted, but this is an important variable for one of my hypotheses.
My questions:
- Am I correct in splitting the dataset for China and Europe? (I did this as I was unsure if a dummy variable would be enough to compare them?)
- Is this the right way to conduct a fixed effects model (with firm, industry and time fixed effects)?
- Is it correct to use firm fixed effects, industry fixed effects AND time fixed effects? Or how do I know what has to be 'fixed'? (I have not been able to find answers to do this online)
This is also my first time using panel data, so any suggestions on how to move forward would be great.
Thank you in advance!!
For my thesis, I am looking at the effect of environmental controversies on the profitability of Chinese and European firms. I am using panel data (2013-2018).
As I want to compare both groups (China vs. Europe), I split the dataset and ran 2 regressions.
After computing the Hausman test for the European dataset, it showed that random effects model is the superior model. Not problems here.
However, for the Chinese dataset it would be beter to use a fixed effects model (according to Hausman).
With panel variable id (company) and time (year) (strongly balanced), i conducted the following fixed effects model:
xtreg ROA EnvCont1_lag EnvCont2_lag CEP1_lag CEP2_lag GUOState OwnCon RD FirmSize Leverage i.Industry i.Year, fe robust
(OwnCon, RD, FirmSize, Leverage and Industry are my control variables)
I included i.Industry and i.Year as I would like to add industry and time fixed effects as well. I think this is the correct way?
However, when I run the regression, the variables OwnCon (time-invariant) and i.Industry are omitted (because of collinearity), and there is barely any significance.
Also, GUOState (dummy for state ownership) is omitted, but this is an important variable for one of my hypotheses.
My questions:
- Am I correct in splitting the dataset for China and Europe? (I did this as I was unsure if a dummy variable would be enough to compare them?)
- Is this the right way to conduct a fixed effects model (with firm, industry and time fixed effects)?
- Is it correct to use firm fixed effects, industry fixed effects AND time fixed effects? Or how do I know what has to be 'fixed'? (I have not been able to find answers to do this online)
This is also my first time using panel data, so any suggestions on how to move forward would be great.
Thank you in advance!!
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