I am studying the determinants of peer group composition and use panel data with a sample period between 2006-2016.
However, I'm facing an issue regarding the measurement of fixed effects.
My dataset looks as following;
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long main_cik double year float(dropped rel_ROA_w isCompPeer main_CEO_turnover rel_at sic_dummy2 sic_dummy3 corrxy main_CEOduality) 2488 2006 0 -.11691028 1 0 -2.679014 1 1 -1 0 2488 2006 0 -.11691028 1 0 -2.679014 1 1 -1 0 2488 2006 0 -.11691028 1 0 -2.679014 1 1 -1 0 2488 2006 0 -.11691028 1 0 -2.679014 1 1 -1 0 2488 2006 0 -.11691028 1 0 -2.679014 1 1 -1 0 2488 2006 0 -.2020019 1 0 -.05955731 1 1 -1 0 2488 2006 0 -.2020019 1 0 -.05955731 1 1 -1 0 2488 2006 0 -.2020019 1 0 -.05955731 1 1 -1 0 2488 2006 0 -.2020019 1 0 -.05955731 1 1 -1 0 2488 2006 0 -.2020019 1 0 -.05955731 1 1 -1 0 2488 2006 0 -.031219125 0 0 .8384784 1 1 1 0 2488 2006 0 -.031219125 0 0 .8384784 1 1 1 0 2488 2006 0 -.031219125 0 0 .8384784 1 1 1 0 2488 2006 0 -.031219125 0 0 .8384784 1 1 1 0 2488 2006 0 -.031219125 0 0 .8384784 1 1 1 0 2969 2015 0 .0041524395 1 0 .5056937 0 0 1 1 2969 2015 0 .0041524395 1 0 .5056937 0 0 1 1 2969 2015 0 .0041524395 1 0 .5056937 0 0 1 1 2969 2015 0 .0041524395 1 0 .5056937 0 0 1 1 2969 2015 0 .0041524395 1 0 .5056937 0 0 1 1 2969 2015 0 .02739468 1 0 -1.360693 1 0 1 1 2969 2015 0 .02739468 1 0 -1.360693 1 0 1 1 2969 2015 0 .02739468 1 0 -1.360693 1 0 1 1 2969 2015 0 .02739468 1 0 -1.360693 1 0 1 1 2969 2015 0 .02739468 1 0 -1.360693 1 0 1 1 2969 2015 0 .019526236 1 0 -.069021285 1 0 -1 1 2969 2015 0 .019526236 1 0 -.069021285 1 0 -1 1 2969 2015 0 .019526236 1 0 -.069021285 1 0 -1 1 2969 2015 0 .019526236 1 0 -.069021285 1 0 -1 1 2969 2015 0 .019526236 1 0 -.069021285 1 0 -1 1 2969 2015 0 -.04745034 1 0 .09800953 0 0 1 1 2969 2015 0 -.04745034 1 0 .09800953 0 0 1 1 2969 2015 0 -.04745034 1 0 .09800953 0 0 1 1 2969 2015 0 -.04745034 1 0 .09800953 0 0 1 1 2969 2015 0 -.04745034 1 0 .09800953 0 0 1 1 2969 2015 0 -.009038955 1 0 .29493254 0 0 1 1 2969 2015 0 -.009038955 1 0 .29493254 0 0 1 1 2969 2015 0 -.009038955 1 0 .29493254 0 0 1 1 2969 2015 0 -.009038955 1 0 .29493254 0 0 1 1 2969 2015 0 -.009038955 1 0 .29493254 0 0 1 1 2969 2015 0 .019391946 1 0 -1.7653357 0 0 1 1 2969 2015 0 .019391946 1 0 -1.7653357 0 0 1 1 2969 2015 0 .019391946 1 0 -1.7653357 0 0 1 1 2969 2015 0 .019391946 1 0 -1.7653357 0 0 1 1 2969 2015 0 .019391946 1 0 -1.7653357 0 0 1 1 2969 2015 0 -.011165775 1 0 -.05051582 1 1 1 1 2969 2015 0 -.011165775 1 0 -.05051582 1 1 1 1 2969 2015 0 -.011165775 1 0 -.05051582 1 1 1 1 2969 2015 0 -.011165775 1 0 -.05051582 1 1 1 1 2969 2015 0 -.011165775 1 0 -.05051582 1 1 1 1 2969 2015 0 .01896139 1 0 .10477632 1 0 1 1 2969 2015 0 .01896139 1 0 .10477632 1 0 1 1 2969 2015 0 .01896139 1 0 .10477632 1 0 1 1 2969 2015 0 .01896139 1 0 .10477632 1 0 1 1 2969 2015 0 .01896139 1 0 .10477632 1 0 1 1 2969 2015 0 -.05593555 1 0 .632718 0 0 1 1 2969 2015 0 -.05593555 1 0 .632718 0 0 1 1 2969 2015 0 -.05593555 1 0 .632718 0 0 1 1 2969 2015 0 -.05593555 1 0 .632718 0 0 1 1 2969 2015 0 -.05593555 1 0 .632718 0 0 1 1 4127 2014 0 .06216906 1 0 -1.3067086 1 1 .4036001 0 4127 2014 0 .06216906 1 0 -1.3067086 1 1 .4036001 0 4127 2014 0 .06216906 1 0 -1.3067086 1 1 .4036001 0 4127 2014 0 .06216906 1 0 -1.3067086 1 1 .4036001 0 4127 2014 0 .06216906 1 0 -1.3067086 1 1 .4036001 0 4127 2015 0 .1159539 1 0 -.8987412 1 1 .4036001 0 4127 2015 0 .1159539 1 0 -.8987412 1 1 .4036001 0 4127 2015 0 .1159539 1 0 -.8987412 1 1 .4036001 0 4127 2015 0 .1159539 1 0 -.8987412 1 1 .4036001 0 4127 2015 0 .1159539 1 0 -.8987412 1 1 .4036001 0 4127 2014 0 .14269203 1 0 .30691504 1 1 1 0 4127 2014 0 .14269203 1 0 .30691504 1 1 1 0 4127 2014 0 .14269203 1 0 .30691504 1 1 1 0 4127 2014 0 .14269203 1 0 .30691504 1 1 1 0 4127 2014 0 .14269203 1 0 .30691504 1 1 1 0 4127 2015 0 .1801022 1 0 .3412647 1 1 1 0 4127 2015 0 .1801022 1 0 .3412647 1 1 1 0 4127 2015 0 .1801022 1 0 .3412647 1 1 1 0 4127 2015 1 .1801022 1 0 .3412647 1 1 1 0 4127 2015 0 .1801022 1 0 .3412647 1 1 1 0 4127 2014 0 .02156958 1 0 -.6470728 1 1 -.9892372 0 4127 2014 0 .02156958 1 0 -.6470728 1 1 -.9892372 0 4127 2014 0 .02156958 1 0 -.6470728 1 1 -.9892372 0 4127 2014 0 .02156958 1 0 -.6470728 1 1 -.9892372 0 4127 2014 0 .02156958 1 0 -.6470728 1 1 -.9892372 0 4127 2015 0 .10041837 1 0 -.29675594 1 1 -.9892372 0 4127 2015 0 .10041837 1 0 -.29675594 1 1 -.9892372 0 4127 2015 0 .10041837 1 0 -.29675594 1 1 -.9892372 0 4127 2015 0 .10041837 1 0 -.29675594 1 1 -.9892372 0 4127 2015 0 .10041837 1 0 -.29675594 1 1 -.9892372 0 4127 2014 0 -.12391421 1 0 .4432786 1 1 -.04774367 0 4127 2014 0 -.12391421 1 0 .4432786 1 1 -.04774367 0 4127 2014 0 -.12391421 1 0 .4432786 1 1 -.04774367 0 4127 2014 0 -.12391421 1 0 .4432786 1 1 -.04774367 0 4127 2014 0 -.12391421 1 0 .4432786 1 1 -.04774367 0 4127 2015 0 -.06187664 1 0 .4934455 1 1 -.04774367 0 4127 2015 0 -.06187664 1 0 .4934455 1 1 -.04774367 0 4127 2015 0 -.06187664 1 0 .4934455 1 1 -.04774367 0 4127 2015 0 -.06187664 1 0 .4934455 1 1 -.04774367 0 4127 2015 0 -.06187664 1 0 .4934455 1 1 -.04774367 0 end
I want to do a logistic regression using industry and year fixed effects (main_cik and year) and cluster by main_cik.
I used the following formula:
areg dropped rel_ROA_w isCompPeer main_CEO_turnover rel_at sic_dummy2 sic_dummy3 corrxy main_CEOduality main_abo_medStockOwn, a(main_cik) cluster(main_cik)
The problem is that I can only include one fixed effect. Therefore, my question is: how can i include fixed effects for both year and main_cik?
Thanks for the help.
Best regards,
Patrick.
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