Help needed with Incremental significance of fixed effects over baseline model

Hello,
I would like to have a similar table as the one attached so that I can have the incremental R2.

. dataex

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Code:

* Example generated by -dataex-. For more info, type help dataex
clear
input double(Lev Roa Delta_Lev Ret) byte(Age Loss Newfin) double ABWC byte(industry_dummy year_dummy Accounting_Firm) int(Office Partner) float panel
.3761747  .0032551 -.0032074 -.044828 20 0 1  .0017319 4  8 62 1945 10418 3
.6068656 -.3427353  .2306909 -.097473 21 1 1 -.1585812 4  9 62 1945 10418 3
.7338428 -.2158798  .1269772    -.308 22 1 0 -.1328195 4 10 62 1945 10418 3
.6529754 -.0228065 -.0808675 -.679926 23 1 0  .0699456 4 11 62 1945  3037 3
.7595537 -.2565636  .1065783 1.971012 24 1 0  .0072353 4 12 62 1945  3037 3
.2000513  .0407306 -.0146887  .055688 10 0 0  .0201577 4  8 42 1264  4473 3
.2002537  .0000734  .0002024  .044118 11 0 0 -.0115693 4  9 42 1264  4473 3
.2532481  .0144371  .0529944 -.190945 12 0 1  .0066421 4 10 42 1264  4473 3
.2341024   .026339 -.0191457 -.133333 13 0 0  -.009283 4 11 42 1264  4473 3
.2360925  .0240422    .00199  .106509 14 0 0  .0095209 4 12 42 1264  6296 3
.0391256  .3189516  .0122431  .353636 20 0 1  .3486014 4  8 42 1264  4473 3
.1773878  .0226219   .054861 -.178303 22 0 1 -.0010174 4 10 62 1945  3037 3
.1931618  .0178606   .028332  .645615 24 0 1  .0589943 4 12 62 1945  3037 3
       .         .         .        .  0 0 1         . 9 12 32  940  7024 7
       0  .2067256         0 1.517638 20 0 0  .0742201 7  8 62 1945 10418 5
       0  .2412522         0  .182884 21 0 0  .0750033 7  9 27  782  8021 5
.1900134  .2319762  .1900134  .047682 22 0 0   .051214 7 10 27  782  8021 5
.1836829  .0322701 -.0063306  .000596 23 0 0 -.0044651 7 11 27  782  8021 5
.1318922  .1736216 -.0517907 -.257919 24 0 0   .057184 7 12 27  782  8021 5
.4176098  .0311269 -.0359335  .073473 12 0 0 -.0612218 5  8 62 1945   124 4
.3471311  .0646509 -.0021686  .331116 15 0 0   .026567 5 11 62 1945  3037 4
.3346072  .0637225 -.0125239  .183675 16 0 0  .0283681 5 12 62 1945  3037 4
       0 -.0695609         0   .31524 20 1 0         . 8  8 62 1945 10418 6
       0  .1594433         0        0 22 0 0         . 8 10 62 1945 10418 6
       0  .1375815         0 -.078432 23 0 0         . 8 11 62 1945 10418 6
       0   .107352         0  .659575 24 0 0         . 8 12 62 1945  6232 6
.4909735  .0317642 -.0277832  .237464 10 0 1  .1345232 8  8 62 1945 10418 6
.4895766  -.015977 -.0013969  .168869 11 1 1  .1858713 8  9 62 1945 10418 6
 .425097   .013343 -.0644795 -.274587 12 0 1   .078994 8 10 62 1945  6232 6
.3762053  .0353974 -.0488918   .22399 13 0 1  .1005442 8 11 42 1264  4473 6
.3552673  .0663567 -.0209379  .131662 14 0 1   .198991 8 12 42 1264  4473 6
 .193627  .0544962  .0123609  .311166  2 0 0  .0328857 9  8 27  782  8021 7
.2897426  .0313513  .0961157  .627744  3 0 1   .048215 9  9 27  782  9844 7
.2855145  .0112423 -.0042281  .434113  4 0 0  .0198614 9 10 27  782  9844 7
.2568752  .0433075 -.0286394 -.407843  5 0 0 -.0314227 9 11 27  782  2986 7
.3303316 -.1614373  .0734565  .115806  6 1 1 -.0873367 9 12 27  782  2986 7
 .057943  .0089309  .0055182        .  0 0 1         . 8 10 62 1945 10418 6
.0323804   .008243 -.0255626        .  1 0 0         . 8 11 62 1945 10418 6
 .026499  .0109464 -.0058815  .241667  2 0 1         . 8 12 62 1945  6232 6
.6664823 -.1034292 -.2668918        .  0 1 1         . 9 12 65 2101  9639 7
       .         .         .        .  0 0 1         . 8  8 62 1945   124 6
       .         .         .        .  1 0 1         . 8  9 62 1945  6232 6
       .         .         .  .133718  2 0 1         . 8 10 62 1945  6232 6
       .         .         .  .205798  3 0 1         . 8 11 62 1945  6232 6
       .         .         .   .04838  4 0 1         . 8 12 62 1945  6232 6
.1512354  .0984355 -.1124697        .  0 0 1         . 5 12 62 1945  6232 4
.5164556 -.0004061 -.0250736        .  0 1 1         . 8 12 32  940  7024 6
.0229742   .010887 -.0138443        .  1 0 1         . 8  8 62 1945   124 6
.0440019  .0148194  .0210277  .208475  2 0 1         . 8  9 62 1945   124 6
.0355736  .0080787 -.0084283 -.026045  3 0 1         . 8 10 62 1946   124 6
.1173472  .0075412  .0817736  .382998  4 0 1         . 8 11 42 1264  2734 6
.0977059  .0085951 -.0196413  .653971  5 0 1         . 8 12 42 1264  2734 6
       0  .0201061         0     .265  2 0 1  .0669373 4  8 32  940   137 3
       0 -.0189554         0  .021591  3 1 1  .1044959 4  9 32  940   137 3
       0 -.0115672         0 -.468663  4 1 1  .1083122 4 10 32  940   137 3
       0    .01685         0  .349126  5 0 1   .095508 4 11 32  940   137 3
       .  .0325612         .     .524  6 0 1  .0912672 4 12 32  940  4842 3
.2132194  .0522128  .0170605   .19248  9 0 1  .0750485 3  8 62 1945   124 2
 .162558  .0706436 -.0506614  .176867 10 0 0 -.0243304 3  9 62 1945   124 2
.2243506  .0570259  .0617926  .056863 11 0 1  .1009437 3 10 62 1945 10418 2
.1125659  .0886425 -.1117847  .155191 12 0 0 -.1061409 3 11 62 1945 10418 2
.1627638   .090892  .0501979  .210785 13 0 1  .1371311 3 12 62 1945  3037 2
.2679027  .0122082  .0697307  .392087 11 0 1  .0833478 3  8 42 1264   708 2
.2305804  .0319716 -.0373223  .035314 12 0 0  .0207737 3  9 42 1264   708 2
.2308907  .0283647  .0003103 -.077381 13 0 0  .0007684 3 10 42 1264   708 2
.1900442   .018037 -.0408465  .355675 14 0 0   .031824 3 11 42 1264   708 2
.1756095 -.0471735 -.0144346  .010606 15 1 0 -.0693055 3 12 42 1264   708 2
 .456402 -.0114397  .0062923 -.338666 10 1 0  .0692985 4  8 62 1945 10418 3
.4587721  -.085108  .0023701  .209677 11 1 1  -.031336 4  9 62 1945 10418 3
 .508821 -.1527041  .0500488     -.25 12 1 0 -.0769263 4 10 62 1945 10418 3
.4215628  .1386689 -.0872581     -.08 13 0 1  .1890865 4 11 62 1945 10418 3
.3997522  .1505265 -.0218106  .666666 14 0 0  .1946961 4 12 62 1945  3037 3
       0  .1743074         0  .138107 11 0 0 -.0026137 9  8 62 1945   124 7
.3623909  .0030553  .0185581     .825  8 0 1  .0548224 1  9 62 1945  6232 1
.3997084 -.0297436  .0338827 -.198347 10 1 0 -.0377198 1 11 42 1264  4473 1
 .448405 -.0921323  .0486966 -.212371 11 1 1 -.0579719 1 12 42 1264  4473 1
.6406786 -.2106569  .1922735 -.078534 12 1 1 -.0953247 1 13 42 1264  4473 1
.5469448 -.0023546  .2188134 -.076577  5 1 1  .0568911 8  8 22  532  2986 6
.4953618  .0676081  -.051583 -.053492  6 0 1  .1362166 8  9 22  532  2986 6
.5771281 -.1264161  .0817663 -.222222  7 1 0 -.1419538 8 10 22  532  2986 6
.7475756 -.3071965  .1704474 -.197279  8 1 1 -.1921422 8 11 22  532  6860 6
.4349489  .2589847 -.3126267 -.368644  9 0 0  .1472318 8 12 27  782  8021 6
.0789214 -.0126858 -.1084148       .8 14 1 1         . 3 12 42 1264  4473 2
 .362568  .0297298 -.0082277  .322953 12 0 1  .0160715 5  8 42 1264  2734 4
.3414154  .0265964 -.0211526  .245365 13 0 0  .0065904 5  9 42 1264  2734 4
.3786342   .032431  .0372189  -.10824 14 0 0  .0040689 5 10 42 1264  2734 4
.4679204 -.0715244  .0892862  .097771 15 1 1  .0061888 5 11 42 1264  2734 4
.4991979 -.0365271  .0312775 -.327309 16 1 1  .0042111 5 12 42 1264   708 4
.2593179  .0738973 -.0019028  .441521 21 0 0  .0058734 8  8 62 1945 10418 6
.2277262  .0729252 -.0315917  .161133 22 0 0  .0038839 8  9 62 1945  3037 6
.3881516  .0588881  .1604254   .00185 23 0 0  .0162373 8 10 62 1945  3037 6
.4433729  .0322523  .0552213  .097492 24 0 1 -.0156108 8 11 62 1945 10418 6
.4402104  .0508907 -.0031625  .086963 25 0 0   -.00171 8 12 62 1945  6232 6
.3399561  .0385674  -.052767        .  0 0 1         . 5  9 42 1264   708 4
.3296723  .0701186 -.0102838        .  1 0 0  .0188054 5 10 42 1264   708 4
.3344046  .0448541  .0047323  .083735  2 0 1  .0131972 5 11 42 1264   708 4
.3233125   .041083 -.0110921 -.087169  3 0 0   .017525 5 12 42 1264  4473 4
       0 -.0016511         .        .  0 1 1         . 9 12 42 1264  4151 7
end

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Array

Below is the code that I am using
egen panel = group(industry_dummy)
xtset industry_dummy
xtreg ABWC Lev Delta_Lev Roa Loss Ret Newfin i.year_dummy i.industry_dummy ,fe
scalar Obs_base_abwc=e(N)
scalar base_abwc = e(r2)
scalar fstat_base_abwc = e(F)
xtreg ABWC Lev Delta_Lev Roa Loss Ret Newfin i.year_dummy i.industry_dummy i.Accounting_Firm, fe
scalar Obs_base_abwc=e(N)
scalar base_abwc = e(r2)
scalar fstat_base_abwc = e(F)
xtreg ABWC Lev Delta_Lev Roa Loss Ret Newfin i.year_dummy i.industry_dummy i.Accounting_Firm i.Office, fe
scalar Obs_base_abwc=e(N)
scalar base_abwc = e(r2)
scalar fstat_base_abwc = e(F)
xtreg ABWC Lev Delta_Lev Roa Loss Ret Newfin i.year_dummy i.industry_dummy i.Accounting_Firm i.Office i.Partner, fe
scalar Obs_base_abwc=e(N)
scalar base_abwc = e(r2)
scalar fstat_base_abwc = e(F)

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Help needed with Incremental significance of fixed effects over baseline model

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Home / Data Cleaning / Data management / Data Processing / Help needed with Incremental significance of fixed effects over baseline model
Help needed with Incremental significance of fixed effects over baseline model