I am having some trouble understanding the true R-squared for my fixed effects model. For the below data I want to perform firm (gvkey) fixed effects regression.
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float(logtobin_w ESGcompensation tenure) double femaledummy float(independent boardsize) double TOTAL_SEC float(stock_executive option_executive logsales leverage roa_w) double(ESGscore year) long gvkey
.11830975 . 7.5 0 .8 10 2477.4176 .2633955 .12278788 7.481996 .26053295 .11522503 . 2010 1004
-.18968147 . 7.727273 0 .8181818 11 2631.8862000000004 .4328289 .07859492 7.637475 .360874 .1014245 . 2011 1004
-.06206616 . 7.727273 0 .8181818 11 1706.0653333333335 .13900936 .1739342 7.681145 .3316019 .11474566 . 2012 1004
-.017516488 . 8.727273 0 .8181818 11 2211.0458333333336 .18035705 .2751986 7.618251 .28824732 .11639009 . 2013 1004
.12475272 . 9.727273 0 .8181818 11 2767.3296000000005 .25445083 0 7.37419 .10165016 .05524752 . 2014 1004
-.016281042 . 10.727273 0 .8181818 11 2072.3328333333334 .2479061 .2186806 7.416138 .10269745 .094931 . 2015 1004
.1741487 0 10.75 .08333333333333333 .8333333 12 2719.3033333333333 .22130963 .13197964 7.477378 .10458081 .09853068 24.84 2016 1004
.3389418 0 9.583333 .08333333333333333 .8333333 12 2596.2338333333328 .22687325 .1721262 7.466399 .11621959 .08296714 23.81 2017 1004
.08878828 0 10.583333 .08333333333333333 .8333333 12 1613.2109999999996 .2315839 .1953064 7.626473 .09339573 .10117321 22.62 2018 1004
.3576642 0 3.181818 .09090909090909091 .9090909 11 7240.1846 .6474589 0 10.621083 .4246824 .18364143 70.36 2015 1045
.3279516 0 3.692308 .15384615384615385 .9230769 13 6626.606833333334 .6696367 0 10.601125 .4747825 .1527675 70.27 2016 1045
.3400276 0 4.6923075 .15384615384615385 .9230769 13 6336.568400000003 .7499329 0 10.65034 .4876839 .13248113 72.02 2017 1045
.2206872 0 5.75 .16666666666666666 .9166667 12 6059.041599999998 .7889238 0 10.704165 .56172 .0925388 69.36 2018 1045
.1878603 0 6.4 .2 0 10 6211.196799999999 .7659218 0 10.73134 .5574465 .10040837 71.34000000000002 2019 1045
.06461501 1 9.636364 .2727272727272727 .9090909 11 3567.396833333333 .2376441 0 8.0906 .298824 .09208658 79.19000000000001 2010 1075
.10431954 1 9.833333 .25 .9166667 12 4634.7656 .36076725 0 8.083755 .26668325 .0895096 72.13 2011 1075
.11441843 1 9.8 .2 .9 10 5922.154799999998 .3203236 0 8.102224 .2551711 .09388095 70.62 2012 1075
.1143407 1 10.8 .2 .9 10 3066.1481666666664 .4162558 0 8.14747 .25835332 .09342367 67.78999999999999 2013 1075
.20094657 1 9.363636 .18181818181818182 .9090909 11 4414.744599999999 .2996751 0 8.158125 .24886835 .08583486 61.63 2014 1075
.15798543 1 10.7 .1 .9 10 4144.8194 .3282692 0 8.159215 .2541859 .08976582 68.98 2015 1075
.217275 1 11.7 .1 .9 10 5279.9712 .3267494 0 8.160142 .27017725 .08384103 62.47000000000001 2016 1075
.2351784 1 11.636364 .18181818181818182 .9090909 11 4392.529 .27997512 0 8.179003 .2918555 .08628815 55.73000000000001 2017 1075
.21915583 1 11.3 .2 .9 10 4146.410833333334 .3376084 0 8.213719 .29520902 .07676775 63.25 2018 1075
.225777 1 11.272727 .18181818181818182 0 11 4581.278166666668 .3218178 0 8.152258 .3145051 .068340935 62.58 2019 1075
.6258997 0 9.833333 .08333333333333333 .9166667 12 12272.8692 .4724094 .05779778 10.467855 .3181561 .17932525 81.90999999999998 2010 1078
.7224569 1 6.8 .2 .9 10 9680.794599999997 .3559583 .072812445 10.567495 .25572947 .19975054 83.65999999999998 2011 1078
.7601307 1 5.090909 .36363636363636365 .9090909 11 11890.849799999998 .3991205 .07130705 10.593477 .3045435 .1869076 80.87 2012 1078
.5852639 1 6.090909 .36363636363636365 .9090909 11 7548.088600000002 .38093635 .2320279 9.991864 .15274835 .10227458 73.87 2013 1078
.7529889 0 7.090909 .36363636363636365 .9090909 11 7549.2126 .2302896 .23029397 9.915762 .1900666 .10841914 68.14999999999999 2014 1078
.736783 0 8.090909 .36363636363636365 .9090909 11 6784.863333333333 .2967345 .29628077 9.923535 .21822193 .11743885 75.64 2015 1078
.5212999 0 9.090909 .36363636363636365 .9090909 11 8267.837400000002 .27706397 .27710316 9.945253 .4178407 .09446322 79.51 2016 1078
.6417528 0 9.090909 .36363636363636365 .8181818 11 9900.922285714287 .19566767 .17468856 10.217934 .3662164 .09202623 77.27 2017 1078
.8903543 0 9.333333 .3333333333333333 .8333333 12 10830.147599999998 .3510712 .3511061 10.328036 .29127774 .112575 72.39 2018 1078
.6530657 0 4.5555553 .1111111111111111 .7777778 9 3883.2235 .28704077 .2051793 8.778634 .4877115 .1629734 67.7 2010 1161
.3645646 0 5.111111 .1111111111111111 .8888889 9 5402.038714285714 .3292796 .3075194 8.789965 .4069439 .1495761 64.90999999999998 2011 1161
.2571971 0 5.6 .1 .8 10 3749.732000000001 .4543789 .22222343 8.59822 .5105 .056 65.69000000000001 2012 1161
.4196974 0 4.6 .1 .7 10 4492.095800000001 .58390486 .1801578 8.5752735 .47452155 .05579894 65.79999999999998 2013 1161
.4057164 0 4.181818 .09090909090909091 .7272727 11 5496.783333333333 .59413034 .1839726 8.613594 .5872047 .073798776 63.76999999999999 2014 1161
.6225287 0 3.4 .2 .6 10 4663.4784 .6716943 .1503329 8.291797 .7275651 .04278551 80.48 2015 1161
1.4030088 0 5 .2222222222222222 .6666667 9 5291.8571999999995 .6315684 .19050725 8.359838 .4320988 .04278551 71.73 2016 1161
1.2907536 0 4.5 .25 .75 8 4795.2928 .56837773 .15451786 8.580919 .3940678 .06468926 69.35000000000001 2017 1161
1.5674034 0 4.111111 .2222222222222222 .7777778 9 5194.050333333334 .5859488 .1699061 8.775703 .2743635 .12949955 68.12 2018 1161
.6420469 1 7.272727 .18181818181818182 .9090909 11 5596.504399999999 .25204903 .1994436 9.107864 .3056664 .1707698 79.81999999999998 2010 1209
.5409696 1 7.818182 .18181818181818182 .9090909 11 5197.334600000001 .2258526 .18627685 9.218507 .3192426 .1750719 84.98 2011 1209
.5037185 1 7.545455 .2727272727272727 .9090909 11 4969.750800000001 .2349808 .18571424 9.170736 .3123576 .13755327 84.45000000000002 2012 1209
.623949 1 8.545455 .2727272727272727 .9090909 11 3123.1486666666674 .23844536 .20301737 9.22822 .3514602 .13531017 75.51 2013 1209
.765121 1 5.416667 .16666666666666666 .9166667 12 5653.788166666666 .24315766 .2003613 9.2533045 .34414 .14402303 78.76 2014 1209
.7700632 1 6.4 .2 .9 10 5296.816 .3667904 .07949234 9.199775 .33713534 .16095217 79.16999999999999 2015 1209
.8828155 1 4.375 .25 .875 8 5156.0642 .3697537 0 9.1616125 .3447852 .16982825 73.57 2016 1209
.8072674 1 5.375 .25 .875 8 6279.959199999998 .4150184 0 9.010376 .21458586 .13681555 79.04 2017 1209
.8526819 1 6.375 .25 .875 8 6434.1898 .4485046 0 9.097194 .1987976 .14923117 84.77999999999999 2018 1209
.17025746 . 11.88889 .2222222222222222 .8888889 9 1961.2088333333336 .3100396 .11475078 8.251221 .3058247 .14258662 . 2010 1230
.252262 . 10.8 .3 .8 10 1907.7008 .2622375 .13603291 8.3705015 .25156882 .14142445 . 2011 1230
.25676554 . 11.8 .3 .8 10 2261.820833333334 .28720525 .08877523 8.446127 .18746594 .14459582 . 2012 1230
.41635615 . 11.727273 .2727272727272727 .8181818 11 2087.1042000000007 .374419 .15566844 8.509967 .14919493 .15690304 . 2013 1230
.6560078 . 10.3 .4 .9 10 1872.3498571428568 .27144995 .14502862 8.588211 .12991425 .1983498 . 2014 1230
.7763644 0 7.454545 .45454545454545453 .9090909 11 2361.6336000000006 .42100295 .10166698 8.630165 .10500536 .2525639 55.67999999999999 2015 1230
.5901425 0 8.454545 .45454545454545453 .9090909 11 2692.6487999999995 .4581262 .08749834 8.687948 .29753062 .18359767 64.86 2016 1230
.4026812 0 9.555555 .4444444444444444 .8888889 9 3092.7891666666665 .5221846 .09153342 8.978786 .23919925 .16294228 60.86999999999999 2017 1230
.2950791 0 7.363636 .45454545454545453 .9090909 11 2582.232833333334 .3301577 .11376386 9.019664 .1927236 .10749634 54.26 2018 1230
.2051185 . 11.666667 .1111111111111111 .7777778 9 1088.4212000000005 .2609991 .1322854 7.451241 .2197327 .08765724 . 2011 1254
.5058599 . 11.5 .1 .8 10 1201.115142857143 .28250012 .1015951 7.352441 .27173635 .14945073 . 2012 1254
.4850742 . 10.857142 .14285714285714285 .8571429 7 1420.2306666666666 .5040103 0 7.400743 .2291917 .13658576 . 2013 1254
.5901712 . 11.857142 .14285714285714285 .8571429 7 1798.992 .326683 0 7.446702 .26651448 .14374375 . 2014 1254
.6101473 0 12.857142 .14285714285714285 .8571429 7 1884.6286000000002 .36298385 0 7.54163 .25745597 .17541023 34.88999999999999 2015 1254
.4182765 0 13.857142 .14285714285714285 .8571429 7 1594.1343333333334 .50208896 0 7.571268 .3666088 .11684445 39.5 2016 1254
.23310487 0 11 .14285714285714285 .7142857 7 2080.5836000000004 .4044619 0 7.624082 .3813571 .09757508 37.67000000000001 2017 1254
.2245757 0 9.857142 .14285714285714285 .8571429 7 2124.7818 .4033992 0 7.706523 .35237 .08998518 39.300000000000004 2018 1254
.28666762 0 6.714286 .2857142857142857 0 7 2073.7504000000004 .4096306 0 7.697621 .4298165 .07229212 50.32 2019 1254
.5978834 1 6.5 .1 .9 10 10855.913199999997 .3528117 .2853227 10.415413 .17560925 .1248348 49.18 2010 1300
.5801171 1 6.3 .1 .9 10 15717.509 0 .2849802 10.50586 .18978597 .1071895 60.96 2011 1300
.6297287 1 7.3 .1 .9 10 13103.977199999998 .28917408 .2767683 10.536487 .17910305 .13205744 63.25 2012 1300
.7847168 1 7 .25 .9166667 12 13788.763600000004 .027408276 .2786295 10.572726 .19432156 .1574777 66.94000000000001 2013 1300
.8463459 1 8 .25 .9166667 12 11117.501333333334 .21651234 .29472128 10.604256 .1910409 .1617786 53.02999999999998 2014 1300
.8096887 1 7.75 .25 .9166667 12 13847.0564 0 .3161889 10.560515 .2447076 .16743045 62.37 2015 1300
.8198145 1 8.75 .25 .9166667 12 11173.043399999997 .3011529 .3437505 10.57903 .29134193 .15441585 66.64999999999999 2016 1300
.9738825 1 9.076923 .23076923076923078 .8461539 13 11548.070999999998 .21835007 .25763392 10.609897 .3011097 .1555391 67 2017 1300
.8560433 1 8.416667 .25 .9166667 12 8241.814571428575 .4841225 .1834174 10.6407 .28065014 .15354924 72.66999999999999 2018 1300
1.0402052 1 9.416667 .25 0 12 9906.926833333333 .3646107 .19597636 10.510777 .28471854 .14460029 74.06999999999998 2019 1300
.9323655 0 8.555555 0 .7777778 9 2140.735 .3200472 .22701335 6.97714 .04778805 .16074465 52.63999999999999 2010 1327
.6521157 0 9.555555 0 .7777778 9 2749.407166666667 .427952 .23105904 7.257653 .013800863 .2039374 46.99 2011 1327
.80136 0 10.555555 0 .7777778 9 1911.8248333333333 .3996799 .26350126 7.357927 0 .1783866 48.730000000000004 2012 1327
.7419222 0 11.625 0 .75 8 2461.6748000000002 .3658626 .2300524 7.491087 0 .19587673 47.07 2013 1327
1.345916 0 10.375 .125 .875 8 3245 .4701273 .15088746 7.736962 0 .2333378 43.57 2014 1327
1.4949583 0 11.375 .125 .875 8 3519.5010000000007 .3934705 .20700005 8.088991 0 .29371822 47.31 2015 1327
1.3172176 0 12.375 .125 .875 8 3090.5408749999992 .4749785 .3120078 8.098339 0 .29371822 41.54 2016 1327
1.4328263 0 12 .1111111111111111 .7777778 9 4661.9418 .5601915 .1291313 8.202866 0 .29371822 55.72000000000001 2017 1327
1.248179 0 13 .1111111111111111 .7777778 9 5194.166166666666 .6930379 0 8.260493 0 .29371822 55.49 2018 1327
1.0762497 0 12 .2222222222222222 0 9 5433.453 .7233093 0 8.124683 0 .29211092 55.11000000000001 2019 1327
.23008296 1 11 .07692307692307693 .7692308 13 7736.2581999999975 .2331519 .2332289 10.43005 .1577297 .17052774 68.46000000000001 2010 1380
.020101056 1 12 .15384615384615385 .7692308 13 7501.111999999998 .2473889 .250891 10.55753 .154768 .1661897 73.16999999999999 2011 1380
-.07162579 1 13 .15384615384615385 .7692308 13 6373.796600000001 .55704385 0 10.537177 .186713 .16677792 69.93000000000004 2012 1380
.05197859 1 5.894737 .10526315789473684 .9473684 19 6965.755199999999 .53402585 0 10.011624 .13561304 .1438228 69.98 2013 1380
-.029052453 1 7.142857 .14285714285714285 .9285714 14 9405.2334 .3771886 .09429847 9.281451 .15519208 .1422054 74.15999999999998 2014 1380
-.17600115 1 8.142858 .14285714285714285 .9285714 14 6649.987200000002 .4433318 .11083187 8.800264 .193888 .05588536 76.76 2015 1380
.1664449 1 9.272727 .18181818181818182 .9090909 11 6101.3272 .432174 .11034811 8.468423 .23779742 .04278551 70.58000000000001 2016 1380
.1561371 1 8.75 .16666666666666666 .9166667 12 7350.4857999999995 .3766926 .1088413 8.606302 .3018778 .04278551 77.38 2017 1380
.09665885 1 9.75 .16666666666666666 .9166667 12 6510.368000000001 .39493415 .12856022 8.751949 .3112957 .11864881 76.59000000000002 2018 1380
.4281915 1 11.181818 .18181818181818182 0 11 8077.180200000002 .3147502 .10647754 8.778788 .3641539 .12744468 78.64 2019 1380
.25403136 . 10.88889 .2222222222222222 .7777778 9 2314.3152000000005 .3474218 .21813995 8.159303 .090723 .10021309 . 2010 1410
end
format %ty year- reg logtobin_w c.ESGcompensation##c.tenure femaledummy independent boardsize TOTAL_SEC stock_executive option_executive logsales leverage roa_w ESGscore i.year i.gvkey
- areg logtobin_w c.ESGcompensation##c.tenure femaledummy independent boardsize TOTAL_SEC stock_executive option_executive logsales leverage roa_w ESGscore i.year, absorb(gvkey)
yields the same estimates in coefficients and P-values. But more importantly, the R-sq. values are the same. However, if I use:
- xtreg logtobin_w c.ESGcompensation##c.tenure femaledummy independent boardsize TOTAL_SEC stock_executive option_executive logsales leverage roa_w ESGscore i.year, fe vce(cluster gvkey)
the R-sq. is significantly lower. It is even lower than other regressions with industry fixed effects. Those have fewer regressors (as #firms > #industries), so per definition the R-sq. for firm fixed effects should be higher.
Does anyone know what is causing this? Thank you in advance.
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