I am working with a dataset that looks like this:
Code:
input float(abs_DACC DISCLOSE TACC_2 firm_id) .01934835 0 .024187315 1 .0574966 0 .01029703 1 .006250922 0 .02406213 1 .015014593 1 -.032378167 1 .00020516732 1 -.03928642 1 .7868658 0 -.032384235 2 1.394589 0 .03529982 2 .4186824 1 -.04371227 2 .0740103 1 -.09670375 2 .315819 1 -.05156721 2 .13176967 1 -.0364581 2 5.007663 0 -2.707317 3 8.962314 0 -127.03571 3 11.19606 1 -17.384615 3 17.230572 1 -26.91398 3 17.230572 1 19.80435 3 .2146687 1 .022880916 3 6.591949e-17 0 .012194293 4 5.551115e-17 0 .05371979 4 0 0 .00599256 4 .05828558 1 -.06137639 4 .00002448961 1 -.4025554 4 .000550691 0 .02795735 5 .0008957673 0 .01801897 5 .00010497781 1 -.007521968 5 .00026854477 1 .04847973 5 2.1900884e-17 1 .0006031727 5 .004420182 0 .0020564343 6 .032602683 0 .04683008 6 .01220035 1 -.036919225 6 .010041647 1 -.008244086 6 .001055236 1 .0010720822 6 .08491836 1 -.0839263 6 .066476375 0 -.09063073 7 .02260962 0 -.07849443 7 .018439792 1 -.018218448 7 .034525417 1 -.10765903 7 .04779583 1 -.0992876 7 .1033026 1 -.13874565 7 .32733145 0 -.06901214 8 .10863945 0 .10752717 8 .24321346 1 -.20360465 8 .06225694 1 -.031496804 8 .07914343 1 -.1281045 8 .013619387 1 -.003827409 8 .01897011 0 -.07032511 9 .3350872 0 -.11521104 9 .1689511 1 -.1888067 9 .9896048 1 -.007527853 9 1.326567 1 .08559322 9 .05096782 1 -.033362597 9 .08315077 0 -.11803018 10 .06128209 0 -.08649246 10 .354572 1 -.09273923 10 .4752698 1 -.07367383 10 .0041804584 1 -.11853525 10 .08981778 1 -.05202269 10 .03780806 0 -.06696881 11 .01859687 0 -.06523953 11 .074236915 0 -.11448495 11 .05487452 1 .07933958 11 .069816515 1 -.05653808 11 .0155515 1 -.06308693 11 1.4021575 0 .010252313 12 .15850054 0 -.07700977 12 .8803007 1 .007802302 12 7.066467 1 -.13872992 12 .4904179 1 -.17439805 12 .11286884 1 -.12696882 12 .012223 0 -.010278583 13 .0011163342 0 -.008919143 13 .010542297 1 -.0209406 13 .005647548 1 -.010411106 13 .0038684416 1 -.14439845 13 .03858467 1 .026925163 13 1.7347235e-18 0 -.009141093 14 6.938894e-18 0 -.04401675 14 2.7755576e-16 1 -.1800494 14 2.428613e-17 1 -.01925059 14 6.800116e-16 1 -.06629362 14 1.3877788e-16 1 .019053344 14 .064121634 1 -.07642028 15 .05395566 0 -.0760234 16 .0471172 0 -.10151234 16 .001719041 1 -.04629297 16 .03862327 1 .003522699 16 .05165628 1 -.087208 16 .009629753 1 -.06834266 16 .04993461 1 -.029572846 17 .03946859 1 -.024929015 17 .07654464 0 -.10461678 18 .05381111 0 -.08892404 18 .01776027 0 -.05072735 18 .030726336 1 -.082152 18 .064680666 1 -.01432552 18 .0471099 1 -.02597645 18 .3535477 0 -.04381294 19 .29521552 0 -.05642635 19 .56696486 1 -.010838772 19 .13899466 1 -.06851529 19
Code:
sum firm_id Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- firm_id | 24,141 2760.679 1674.156 1 6233 . reghdfe abs_DACC DISCLOSE TACC_2, absorb(firm_id) (dropped 713 singleton observations) (MWFE estimator converged in 1 iterations) HDFE Linear regression Number of obs = 23,428 Absorbing 1 HDFE group F( 2, 18670) = 11.20 Prob > F = 0.0000 R-squared = 0.5093 Adj R-squared = 0.3843 Within R-sq. = 0.0012 Root MSE = 1.6299 ------------------------------------------------------------------------------ abs_DACC | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- DISCLOSE | .0575487 .023677 2.43 0.015 .0111396 .1039579 TACC_2 | -.0005184 .000127 -4.08 0.000 -.0007674 -.0002694 _cons | .4833111 .0185127 26.11 0.000 .4470246 .5195977 ------------------------------------------------------------------------------ Absorbed degrees of freedom: -----------------------------------------------------+ Absorbed FE | Categories - Redundant = Num. Coefs | -------------+---------------------------------------| firm_id | 4756 0 4756 | -----------------------------------------------------+
Sarah
0 Response to Firm fixed effects with reghdfe
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