(this is a re-post of a previous post I have posted by accident with a title that I was not yet done writing)
I am dealing w/ panel data and am running 3 regressions based on a dummy variable that indicates credit rating category. (ratings are AAA, AA, A, BBB, BB, B, CCC)
I hypothesize that the effect of my dependant variable (earnings management) is different for BBB- and BB-rated firms, compared to other categories. This hypothesis is supported in a regression on a dummy that groups both categories (dummy is BORDERLINE=1 if BBB or BB, 0 otherwise)
Now I want to check if there's a difference BETWEEN BBB and BB-rated firms. I run a regression on a dummy for BBB (=0 if BB and =1 if BBB). I find no statistically sign differences. The same goes when i drop BBB and regress on BB (=1 if BB, 0 otherwise): the coefficient on BB is significant just like in the original model.
BUT if i regress on BB, and BBB is still in the sample, the coefficient on BB becomes insignificant. This seems to be suggesting to me that this result is driven by the BBB group.
How can this be the case? Am I not essentially running 2 identical tests?
Code:
g BBB = 0 if rating =="BB" replace BBB = 1 if rating == "BBB" g POST_BBB = POST*BBB
Code:
reghdfe ABPROD BBB POST POST_BBB DSIZE DLEV DBTM DROA DMKTCAP ABACC, a(sic) cluster(gvkey) reghdfe ABCFO BBB POST POST_BBB DSIZE DLEV DBTM DROA DMKTCAP ABACC, a(sic) cluster(gvkey) reghdfe ABDEXP BBB POST POST_BBB DSIZE DLEV DBTM DROA DMKTCAP ABACC, a(sic) cluster(gvkey)
Code:
g BB = 0 replace BB =1 if rating == "BB" g POST_BB = POST* BB drop if rating ==. "BBB" reghdfe ABPROD BB POST POST_BB POST_BBB DSIZE DLEV DBTM DROA DMKTCAP ABACC, a(sic) cluster(gvkey) reghdfe ABCFO BB POST POST_BB POST_BBB DSIZE DLEV DBTM DROA DMKTCAP ABACC, a(sic) cluster(gvkey) reghdfe ABDEXP BB POST POST_BB POST_BBB DSIZE DLEV DBTM DROA DMKTCAP ABACC, a(sic) cluster(gvkey)
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input double year long gvkey float(ABPROD ABCFO ABDEXP BBB BB POST POST_BB POST_BBB DLEV DBTM DROA DMKTCAP ABACC) 2008 3505 -1.730013 .22883296 -.445899 1 0 0 0 0 -.13215522 .096816 -.0045735687 .6231031 .1947115 2009 20338 -1.0557207 .14567 .3097367 . 0 0 0 . -.05029216 .1345559 .031757746 -2.2393432 -.030602077 2008 2710 -.9025259 .11560844 .25538284 0 1 0 0 0 .18613347 .09934908 -.06429902 -.5229578 -.021254357 2012 4809 -.9267323 .1363228 .2330895 1 0 1 0 1 -.017841667 -.15771052 .016152345 -.9167547 -.022674557 2011 2710 -.9636471 .04666772 .3686053 0 1 1 1 0 .06988898 .7642462 -.0021777116 -.3876104 -.05210483 2012 28877 -.9511288 .1229403 .2999154 . 0 1 0 . -.14160061 .199282 .04195441 -.7976294 -.04487421 2011 5071 1.7203083 -.57618093 -.9580992 1 0 1 0 1 .02139908 .39236495 .01966463 1.3787603 -.06815204 2011 3144 .7380345 -1.0956876 -2.419925 . 0 1 0 . -.0999229 .3334708 .05328922 2.6230116 .12199827 2013 5597 -.7742254 .11469482 .2066243 . 0 1 0 . .06397626 -.2850582 .0866582 .7101917 -.069256335 2014 3144 1.4164505 -1.3977017 -2.7003524 . 0 1 0 . -.08138417 -.14122604 .009399667 2.6230116 -.362417 2006 2710 -.80946 .13914369 .2588311 0 1 0 0 0 .14156628 .25987574 -.02313412 -.3556175 .01290969 2015 66016 -.59515893 .0835389 .3372578 1 0 1 0 1 .04289544 .007315516 .00601726 -.6332006 .007224321 2009 1722 -3.7542174 .54324657 1.0815662 . 0 0 0 . -.0702198 .3303154 -.01552239 1.0285311 -1.1522968 2011 4809 -1.0648366 .14565884 .245658 1 0 1 0 1 -.08830622 .4277507 .02885619 -.8849287 -.009079384 2010 1722 -.032900274 .10232128 .24082494 . 0 0 0 . -.0996779 .4534232 -.018821284 .9854231 -.00347677 2007 2435 -1.412651 .09656568 .17901623 . 0 0 0 . -.14368808 -.17598993 .06204604 .0704689 -.05202331 2011 62655 -.58898 .10962556 .169532 . 0 1 0 . .26993334 .08343875 -.14204419 -1.1113615 -.012227118 2010 2710 -.7130704 .00961098 .57386047 0 1 0 0 0 .12144867 .16270766 -.01080352 -.3613949 .062853195 2007 3144 -.8570452 -.23945427 -.26681638 . 0 0 0 . -.19041952 -.2334358 .10886978 2.6230116 .120211 2002 2663 . .07254245 .20301346 . 0 0 0 . .12250859 -.3862304 -.009216726 .5311594 -.0829592 2011 1722 4.7638335 .54324657 1.0235693 . 0 1 0 . -.07476912 1.056484 .00027392805 1.1815481 .9877299 2013 8479 5.746288 -1.254217 -2.544613 . 0 1 0 . .042968 -.1676498 .0044059902 2.472422 .08780587 2009 180833 -.9229865 .11269227 .2194353 1 0 0 0 0 .26993334 -.53065443 -.020774815 .3745766 -.006197353 2004 6375 -.8180135 .05962986 .02691719 1 0 0 0 0 .11581494 -.2289426 .008571558 .8633394 -.00568961 2002 62655 -.6579787 .11485443 .19290254 0 1 0 0 0 .17142415 .12539965 -.007859781 -.5352402 -.09105141 2004 20338 -.7675316 .1200235 .2331526 0 1 0 0 0 .021433637 -.09247097 .007678263 -1.482365 -.009688833 2012 3144 -.1700754 -.4487998 -.9184915 . 0 1 0 . -.1149032 -.2221547 .04133243 2.6230116 -.015580602 2002 5071 -.6319818 .03140664 .10697074 . 0 0 0 . .10679188 -.13308746 -.14204419 1.1255074 -.017894562 2005 5597 -1.0080678 .13869047 .3304078 . 0 0 0 . -.03819533 -.3107275 .06443333 .6396093 .07273462 2013 2435 -1.951063 -.02046594 .02465558 . 0 1 0 . -.0281024 -.25267512 .09584643 .5820551 .011755 2003 4809 -1.1095296 .1714203 .3108006 1 0 0 0 0 -.214898 .12556088 -.02793713 -1.7520466 .003341782 2013 144435 5.418912 .0545868 .3979453 1 0 1 0 1 -.15239178 .39660195 -.07823525 .12583447 -.12968299 2014 28877 -.9124427 .08354153 .26902157 0 1 1 1 0 -.214898 -.14196701 .08916429 .529768 -.06085035 2008 2663 -.57152605 .09286155 .1669768 . 0 0 0 . -.05575982 -.4661626 .0697944 .9874754 -.00752483 2007 3505 -.7317346 .10786648 .18470916 1 0 0 0 0 -.09810054 .3799539 -.017490085 .19929504 .005468682 2011 2663 1.8416433 -.58651567 -.34093165 . 0 1 0 . .08300856 .23655626 .06400376 .5255127 .28026742 2002 5597 -.8948669 .08981483 .2983295 . 0 0 0 . -.06083205 -.2227577 -.10242002 .4772673 -.01636668 2006 66016 -.787026 .13908507 .3490902 1 0 0 0 0 -.07172075 .16657475 -.016077552 -1.1363239 .018990524 2010 142953 3.1519785 -.8010868 -1.6347816 . 0 0 0 . -.03430456 .2185367 -.04290253 2.19094 .5857763 2015 2710 -.654348 .04938512 .20283793 0 1 1 1 0 .08618128 -.07251944 -.003289968 .7802038 -.017022766 2014 9411 -.7874458 .11663897 .23835585 1 0 1 0 1 .021283835 -.22309074 .017680593 -.4780588 -.015157825 2003 1722 2.3755054 .11227222 .37371495 . 0 0 0 . -.04712483 .4720395 -.04683506 .23031616 .074134275 2013 179700 -.8611708 .10093424 .13079064 1 0 1 0 1 .034721017 -.12271167 -.016004063 -.10380936 -.021387257 2002 3144 .06170797 -1.3977017 -2.7003524 . 0 0 0 . -.195323 -.26536828 .10886978 2.6230116 -.0543924 2010 9774 -.6376042 .11262868 .2422876 . 0 0 0 . -.0562349 .47409275 -.012342013 -.4536104 -.005871306 2016 3505 -.8178222 .09046821 .18760957 1 0 1 0 1 .04438335 .2566283 .09078398 .3819532 -.005692217 2007 4809 -1.0886261 .13950287 .26554468 1 0 0 0 0 -.214898 -.0812833 .04250089 -1.267827 -.02390835 2016 4809 -.8943385 .13436177 .2017637 1 0 1 0 1 -.0008630455 .0013207495 -.013631701 -1.23232 -.01432232 2010 20338 -1.1838899 .18381703 .4809025 . 0 0 0 . .07978526 .18608585 -.017326102 -1.9864936 -.02375149 2006 3138 -1.2538137 .1690378 .3316411 1 0 0 0 0 .2364482 -.20953003 -.05066036 -2.3688588 .014244232 2015 4809 -.9469118 .13136438 .21634015 1 0 1 0 1 .008102238 -.032543182 .005590305 -1.0417709 -.02273758 2007 5071 2.825689 -.6848263 -1.192579 1 0 0 0 0 -.03777611 -.0948762 .009337235 1.026124 .08999076 2013 180833 -.9467896 .10525902 .18562423 1 0 1 0 1 .01936838 -.3415318 .10886978 .4581585 -.07219241 2013 7146 -.8784832 .12008923 .22521234 . 0 1 0 . -.04209113 -.1451455 .007803947 -.16799736 -.029150855 2010 6375 1.0389624 -.10455126 -1.728463 1 0 0 0 0 .0983619 -.3164702 .03141305 1.109313 .56998026 2009 66016 -.7760103 .04819685 .8630474 1 0 0 0 0 -.1789611 .3179875 -.04880327 -1.0388756 -.13619408 2007 144435 2.3733892 .54324657 1.0815662 1 0 0 0 0 -.10995477 .06666309 -.007635355 .6130295 .8382236 2004 1722 .1779779 .14659788 .259101 . 0 0 0 . -.05211303 .3536803 -.04968844 .3390703 .05034481 2014 5071 .8618653 -.3313862 -.45152885 1 0 1 0 1 .05782455 -.1570753 -.01665309 .9944639 -.13128573 2015 5071 -.58043283 .08281563 .19255076 1 0 1 0 1 .009456545 -.17346604 .00433363 1.0640192 -.024917094 2009 142953 4.823379 -1.0167964 -2.263422 . 0 0 0 . -.04700428 .18904576 -.013694722 1.87751 -.1081434 2003 6375 .3449812 -.5663903 -1.7900246 1 0 0 0 0 .1444354 -.28723982 .0009216666 .8615055 .17977592 2005 20338 -1.433635 .23219413 .6211026 0 1 0 0 0 -.02522695 .069574445 -.04138167 -1.8945732 -.007753203 2011 7146 -1.2272536 .21372065 .3633187 . 0 1 0 . -.018782526 .3811545 .04539609 .034446716 .05645994 2013 66016 -.481824 .09958082 .34628385 1 0 1 0 1 .0492405 .1137682 -.015081085 -.743556 .031854335 2015 163983 -.9330289 .1222281 .27316338 0 1 1 1 0 .014352083 .2431268 -.04350419 -1.3404846 .003842002 2003 3138 -1.0728301 .1491575 .2985037 1 0 0 0 0 .26993334 -.2699734 -.05341553 -2.3688588 .015960941 2013 3138 -1.4982126 .22132698 .4197696 1 0 1 0 1 .07182199 -.07621217 -.06433397 -2.3688588 .01503241 2007 6375 1.403365 -.6208221 -2.3046465 1 0 0 0 0 .02128902 -.26276883 .04108669 .9840107 .1816351 2010 3138 -1.514615 .22115454 .4302421 1 0 0 0 0 .12620062 -.18152496 -.05182426 -2.3688588 .01162897 2012 66016 -.9249421 .1148439 .23732775 1 0 1 0 1 .02239153 .06645611 .009054802 -.4833498 -.01477541 2002 4809 -1.466391 .21207374 .3986382 1 0 0 0 0 -.10204548 .6393586 -.14204419 -2.261827 .009297717 2012 7146 -.7373758 .10935561 .2261575 . 0 1 0 . -.09884118 -.22782677 .028783344 .06102753 -.031434696 2012 180833 -1.1367524 .09710814 .16750085 1 0 1 0 1 .18159133 -.423159 .10886978 .5055771 -.07070819 2009 2435 -.6444438 .09677543 .219849 . 0 0 0 . -.1670099 -.2335045 .06762083 .3303833 -.04555846 2016 66016 -.7949565 .10771704 .2439881 1 0 1 0 1 -.023767173 -.005948275 .025188833 -.4557276 -.02594327 2009 10793 .9015601 .11660357 .2754746 0 1 0 0 0 -.00259012 .46129265 -.11098873 -.25767517 -.029970646 2007 2710 -.7016325 .12001009 .24744987 0 1 0 0 0 .19627336 .28013387 -.12684333 -.6107912 -.014354423 2013 62655 -.5664123 .15681267 .2084759 . 0 1 0 . .04887241 .0794129 -.028686445 -1.8816404 .04118545 2009 9774 1.2705243 .2333047 .4264712 . 0 0 0 . .06140295 .4306932 -.07566625 -.6807156 .0229408 2014 2663 -.1749356 .0038176775 -.036435425 1 0 1 0 1 -.011945784 -.18164924 .01913097 .04573917 .09117487 2015 3505 -1.2461407 .04657549 .18798438 1 0 1 0 1 -.07866263 .1013929 -.0475389 .2928934 -.02156809 2009 3144 -.4401772 -.7597789 -1.2649486 . 0 0 0 . -.21322967 -.2662194 .106827 2.6230116 -.06099894 2005 3144 -.650002 -.6261171 -1.042334 . 0 0 0 . -.214898 -.2162974 .09006868 2.611744 -.1543398 2002 2435 . . . . 0 0 0 . -.027736574 -.19309846 -.14204419 -.19188786 . 2012 3505 -.981407 .029847905 .19623047 1 0 1 0 1 -.0747996 .6012578 -.035905086 -.03558922 -.04627528 2016 2710 -.6450307 -.06000477 .14500278 0 1 1 1 0 .07131219 -.06642078 .02008979 .7725134 .016402211 2005 2435 -.9606452 .12031393 .2303767 . 0 0 0 . -.12901816 -.21625085 .08508142 .263546 -.05317856 2007 66016 -.9909706 .18789802 .4673594 1 0 0 0 0 -.09889755 .2099293 .012517449 -1.0437155 .05662382 2009 2710 -.8668887 .12526816 .26972073 0 1 0 0 0 .08769268 .3156319 -.04923168 -.6088591 .003017541 2015 20338 -1.1131009 .16165927 .3248849 . 0 1 0 . .2127148 .22524476 -.06827979 -2.3688588 -.006751105 2010 160163 -1.991752 .29392067 .7320628 . 0 0 0 . .26993334 -.5351758 -.14204419 -2.3688588 .04711248 2014 180833 -.9098877 .0946689 .14500786 1 0 1 0 1 .10968697 -.27747533 .10886978 .4919834 -.06882714 2015 179700 -1.1999992 -.035416216 -.264763 1 0 1 0 1 .008563966 -.1803596 .01940299 .3031492 -.02828071 2004 62655 2.2606955 .199675 -.1164384 0 1 0 0 0 .15653814 .18990925 -.03766808 -.458807 -.00028635561 2010 3144 -.27918363 -.6030802 -.941754 . 0 0 0 . -.12362257 -.22636005 .10886978 2.6230116 .2812666 2012 2435 -.8729183 .1120367 .2061194 . 0 1 0 . -.010947406 -.3165051 .0985324 .6270685 -.06201785 2008 1408 3.0202446 -.4612848 -.53169274 1 0 0 0 0 .07974762 .1874525 -.022953764 .2502184 -.8367664 2011 20338 -.327581 .05961342 .24254395 . 0 1 0 . .12835401 1.073942 -.03967463 -2.3588223 .0356455 2012 29942 -.8010333 .14783719 .4046384 1 0 1 0 1 .1314069 -.014515072 -.06226857 -.6946487 -.019601997 end
0 Response to dummy in OLS regression biased by other categorical variable in the sample
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