Hi please
I need to examine the impact of hard_final_Exact_new (and csopresence1) on two dependent variables: ENVIRONMENTAL_SCORE_w and SOCIAL_SCORE_w. I have estimated separate regressions as follows:
these are the main regression
reg ENVIRONMENTAL_SCORE_w hard_final_Exact_new csopresence1 dFreezeXCSO Firm_Size_w ROA_w Leverage_w Market_book_four_w Non_pension_CFO_w STD_CFO_w board_size_w GenderRatiogenderratio_w independent_percentage_w SUSTAIBILITY_COMITEE_FU Fund_Status_w FUNDING_RATIO_w Platn_Size_w i.year i.ff_12 , robust cluster (id) nest replace drop(i.year i.ff_12 ) dec(4) save(qqqkkq)
reg SOCIAL_SCORE_w hard_final_Exact_new csopresence1 dFreezeXCSO Firm_Size_w ROA_w Leverage_w Market_book_four_w Non_pension_CFO_w STD_CFO_w board_size_w GenderRatiogenderratio_w independent_percentage_w SUSTAIBILITY_COMITEE_FU Fund_Status_w FUNDING_RATIO_w Platn_Size_w i.year i.ff_12 , robust cluster (id) nest replace drop(i.year i.ff_12 ) dec(4) save(qqqkkq)
To test whether the coefficient on hard_final_Exact_new is equal across the two equations, I then estimated a seemingly unrelated regression (sureg) and ran a Wald test as follows:
I would appreciate it if someone could kindly confirm .Is this the correct way to test for equality of coefficients across the two equations
Many thanks in advance for your help!
I need to examine the impact of hard_final_Exact_new (and csopresence1) on two dependent variables: ENVIRONMENTAL_SCORE_w and SOCIAL_SCORE_w. I have estimated separate regressions as follows:
these are the main regression
reg ENVIRONMENTAL_SCORE_w hard_final_Exact_new csopresence1 dFreezeXCSO Firm_Size_w ROA_w Leverage_w Market_book_four_w Non_pension_CFO_w STD_CFO_w board_size_w GenderRatiogenderratio_w independent_percentage_w SUSTAIBILITY_COMITEE_FU Fund_Status_w FUNDING_RATIO_w Platn_Size_w i.year i.ff_12 , robust cluster (id) nest replace drop(i.year i.ff_12 ) dec(4) save(qqqkkq)
reg SOCIAL_SCORE_w hard_final_Exact_new csopresence1 dFreezeXCSO Firm_Size_w ROA_w Leverage_w Market_book_four_w Non_pension_CFO_w STD_CFO_w board_size_w GenderRatiogenderratio_w independent_percentage_w SUSTAIBILITY_COMITEE_FU Fund_Status_w FUNDING_RATIO_w Platn_Size_w i.year i.ff_12 , robust cluster (id) nest replace drop(i.year i.ff_12 ) dec(4) save(qqqkkq)
To test whether the coefficient on hard_final_Exact_new is equal across the two equations, I then estimated a seemingly unrelated regression (sureg) and ran a Wald test as follows:
HTML Code:
. sureg (ENVIRONMENTAL_SCORE_w = hard_final_Exact_new csopresence1 dFreezeXCSO Firm_
> Size_w ROA_w Leverage_w Market_book_four_w Non_pension_CFO_w STD_CFO_w board_size_
> w GenderRatiogenderratio_w independent_percentage_w SUSTAIBILITY_COMITEE_FU Fund_S
> tatus_w FUNDING_RATIO_w Platn_Size_w i.year i.ff_12)(SOCIAL_SCORE_w = hard_final_E
> xact_new csopresence1 dFreezeXCSO Firm_Size_w ROA_w Leverage_w Market_book_four_w
> Non_pension_CFO_w STD_CFO_w board_size_w GenderRatiogenderratio_w independent_perc
> entage_w SUSTAIBILITY_COMITEE_FU Fund_Status_w FUNDING_RATIO_w Platn_Size_w i.year
> i.ff_12),
Seemingly unrelated regression
------------------------------------------------------------------------------
Equation Obs Params RMSE "R-squared" chi2 P>chi2
------------------------------------------------------------------------------
ENVIRONMEN~w 1,389 34 1.462768 0.4629 1197.21 0.0000
SOCIAL_SCO~w 1,389 34 1.391546 0.3686 810.98 0.0000
------------------------------------------------------------------------------
-----------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
------------------+----------------------------------------------------------------
ENVIRONMENTAL_S~w |
hard_final_Exac~w | 1.47537 .4734705 3.12 0.002 .547385 2.403355
csopresence1 | .474246 .0900539 5.27 0.000 .2977436 .6507484
dFreezeXCSO | 1.301604 .5756402 2.26 0.024 .1733696 2.429838
Firm_Size_w | .5163808 .0678514 7.61 0.000 .3833945 .6493672
ROA_w | -2.584431 1.161321 -2.23 0.026 -4.860578 -.3082843
Leverage_w | .8486148 .3462929 2.45 0.014 .1698932 1.527336
Market_book_fou~w | .015872 .0055308 2.87 0.004 .0050318 .0267122
Non_pension_CFO_w | 8.426989 1.499672 5.62 0.000 5.487685 11.36629
STD_CFO_w | 14.25217 2.720208 5.24 0.000 8.920658 19.58368
board_size_w | -.0005482 .0233207 -0.02 0.981 -.046256 .0451595
GenderRatiogend~w | -.5176502 .5511037 -0.94 0.348 -1.597794 .5624932
independent_per~w | 1.582114 .6059581 2.61 0.009 .3944575 2.769769
SUSTAIBILITY_CO~U | -.241673 .0966839 -2.50 0.012 -.4311701 -.052176
Fund_Status_w | 2.855814 1.815546 1.57 0.116 -.7025905 6.414218
FUNDING_RATIO_w | .3951698 .3165965 1.25 0.212 -.2253479 1.015687
Platn_Size_w | -.0241221 .0498191 -0.48 0.628 -.1217657 .0735215
|
year |
2016 | .2338439 .1550618 1.51 0.132 -.0700717 .5377595
2017 | .4155719 .1565518 2.65 0.008 .1087359 .7224079
2018 | .5248613 .1585057 3.31 0.001 .2141958 .8355268
2019 | .8417459 .1620552 5.19 0.000 .5241235 1.159368
2020 | 1.338726 .1679686 7.97 0.000 1.009513 1.667938
2021 | 1.41018 .1733559 8.13 0.000 1.070409 1.749952
2022 | 1.666995 .1866707 8.93 0.000 1.301127 2.032863
|
ff_12 |
2 | -.0867583 .3649606 -0.24 0.812 -.8020679 .6285513
3 | .0632768 .1844413 0.34 0.732 -.2982216 .4247752
4 | -.2027057 .2466964 -0.82 0.411 -.6862217 .2808102
5 | .7284681 .2177962 3.34 0.001 .3015954 1.155341
6 | -.5378867 .195127 -2.76 0.006 -.9203286 -.1554447
7 | -.722912 .3518839 -2.05 0.040 -1.412592 -.0332322
8 | .4506197 .1868437 2.41 0.016 .0844127 .8168266
9 | -1.801293 .2744625 -6.56 0.000 -2.33923 -1.263357
10 | -.743844 .1972408 -3.77 0.000 -1.130429 -.3572592
11 | -2.25066 .2243409 -10.03 0.000 -2.690361 -1.81096
12 | .6337655 .2021632 3.13 0.002 .2375329 1.029998
|
_cons | -5.134967 .9275181 -5.54 0.000 -6.952869 -3.317065
------------------+----------------------------------------------------------------
SOCIAL_SCORE_w |
hard_final_Exac~w | 1.455826 .4504173 3.23 0.001 .5730242 2.338628
csopresence1 | .2556976 .0856692 2.98 0.003 .087789 .4236061
dFreezeXCSO | 1.733049 .5476124 3.16 0.002 .6597481 2.806349
Firm_Size_w | -.0101615 .0645478 -0.16 0.875 -.1366728 .1163498
ROA_w | -.439576 1.104776 -0.40 0.691 -2.604898 1.725746
Leverage_w | .1962526 .329432 0.60 0.551 -.4494222 .8419275
Market_book_fou~w | .0013566 .0052615 0.26 0.797 -.0089558 .011669
Non_pension_CFO_w | .6085714 1.426654 0.43 0.670 -2.187619 3.404762
STD_CFO_w | 1.203728 2.587762 0.47 0.642 -3.868192 6.275647
board_size_w | -.0480787 .0221852 -2.17 0.030 -.091561 -.0045965
GenderRatiogend~w | 1.366735 .5242706 2.61 0.009 .3391837 2.394287
independent_per~w | 3.793739 .5764541 6.58 0.000 2.66391 4.923568
SUSTAIBILITY_CO~U | .1269823 .0919764 1.38 0.167 -.0532882 .3072528
Fund_Status_w | .4813046 1.727147 0.28 0.780 -2.903842 3.866451
FUNDING_RATIO_w | .4489992 .3011815 1.49 0.136 -.1413056 1.039304
Platn_Size_w | .1187499 .0473934 2.51 0.012 .0258606 .2116393
|
year |
2016 | .2072111 .1475119 1.40 0.160 -.0819069 .4963291
2017 | .3655264 .1489294 2.45 0.014 .0736302 .6574226
2018 | .6257023 .1507881 4.15 0.000 .330163 .9212417
2019 | .8751101 .1541648 5.68 0.000 .5729527 1.177268
2020 | 1.422739 .1597902 8.90 0.000 1.109556 1.735922
2021 | 1.430727 .1649153 8.68 0.000 1.107499 1.753955
2022 | 1.59841 .1775818 9.00 0.000 1.250356 1.946464
|
ff_12 |
2 | .4542372 .3471908 1.31 0.191 -.2262443 1.134719
3 | .0887362 .1754609 0.51 0.613 -.255161 .4326333
4 | .8750006 .2346848 3.73 0.000 .4150268 1.334974
5 | .7366388 .2071917 3.56 0.000 .3305505 1.142727
6 | .2219267 .1856263 1.20 0.232 -.1418942 .5857477
7 | -.8821803 .3347508 -2.64 0.008 -1.53828 -.2260808
8 | 1.147947 .1777464 6.46 0.000 .7995701 1.496323
9 | -.7231151 .261099 -2.77 0.006 -1.23486 -.2113705
10 | -.1359631 .1876372 -0.72 0.469 -.5037252 .231799
11 | -.6717386 .2134178 -3.15 0.002 -1.09003 -.2534473
12 | -.1546695 .1923199 -0.80 0.421 -.5316097 .2222706
|
_cons | -2.999039 .8823575 -3.40 0.001 -4.728427 -1.26965
-----------------------------------------------------------------------------------
.
.
. test [ENVIRONMENTAL_SCORE_w]hard_final_Exact_new = [SOCIAL_SCORE_w]hard_final_Exac
> t_new
( 1) [ENVIRONMENTAL_SCORE_w]hard_final_Exact_new -
[SOCIAL_SCORE_w]hard_final_Exact_new = 0
chi2( 1) = 0.00
Prob > chi2 = 0.9736
.
end of do-file
Many thanks in advance for your help!
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