Hello,
I am trying to study the effect of gender (and other accounting and market variables) on the probability of filing for bankruptcy in the previous period and it was suggested by my advisor that I should use -logit, vce(cluster)- and -xtlogit, fe- , since I am using panel data (N=13006, T=40).
I had asked on the possibility of using -logit- instead of -xtlogit- with panel data before in this post and I was told that no, I couldn't. But I found later that day a lot of posts where it was suggested to people to use standard error clustering with -logit- instead of -xtlogit- and when I discussed that with my advisor, his opinion was that I should present my results for both.
However, I find very different (and both significant) results for both functions.
For instance, for -logit, vce(cluster id)-, I get:
How can I justify these differences? And how can I be sure on which results should I focus on?
Thank you so much in advance!
Best,
Sofi
I am trying to study the effect of gender (and other accounting and market variables) on the probability of filing for bankruptcy in the previous period and it was suggested by my advisor that I should use -logit, vce(cluster)- and -xtlogit, fe- , since I am using panel data (N=13006, T=40).
I had asked on the possibility of using -logit- instead of -xtlogit- with panel data before in this post and I was told that no, I couldn't. But I found later that day a lot of posts where it was suggested to people to use standard error clustering with -logit- instead of -xtlogit- and when I discussed that with my advisor, his opinion was that I should present my results for both.
However, I find very different (and both significant) results for both functions.
For instance, for -logit, vce(cluster id)-, I get:
- gender coefficient of 0.49 with z = 2.09
- => -margins, dydx(gender) atmeans- of 0.0000459 with z=2.01
- r-squared of 0.2487
- gender coefficient of -1.47 with z = -1.74
- r-squared of 0.5785
How can I justify these differences? And how can I be sure on which results should I focus on?
Thank you so much in advance!
Best,
Sofi
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