HI Helpful List,
I have panel data on ~2000 companies between 2005 and 2010. Of these 2000 companies, about 100 went bankrupt in some year between 2005 and 2010. I have various variables in the dataset, including financial leverage, performance, shareholders, CEO data, etc. I am trying to test the significance of the various variables in predicting company bankruptcy (the outcome variable).
I am trying to decide whether fixed effects (FE) or random effects (RE) models make more sense in my case. I like FE because this controls for omitted variables, but the major limitation I am facing is that FE requires the outcome variable to vary over time (i.e., companies to go bankrupt at some point in time). Therefore, all companies that never go bankrupt in my sample (about 95%) are excluded from the models when I use FE. This limits my sample considerably, and I would think does not make theoretical sense (is there not something important in the data on companies that never go bankrupt?). RE does not have this same requirement and so I can run regressions (using xtlogit, re) with RE for my full sample (~2000) companies.
Am I thinking about this the right way? Any advice on how to go about deciding between FE and RE in this specific situation would be appreciated. Maybe the ideal solution is to report results using both FE and RE; however, if this is the case, I would still like a some stronger footing to know which results to lead with... what makes the most sense...
In advance, thank you.
Roger
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