Hi
im using simple probit model
where my independent variablecsopresence1 is dummy , my dependent variable hard_final_Exact_new is dummy and Moderator is dummy , which is calculated as Modriatr = csopresence1 * post_SFAS158 * PENADJ_indicator
and i have some control variable that is dummy post_SFAS158 PENADJ_indicator
the regression give me
note: Modriatr != 0 predicts failure perfectly;
Modriatr omitted and 8 obs not used.
this is my model
probit hard_final_Exact_new csopresence1 Modriatr post_SFAS158 PENADJ_indicator Firm_Size_w ROA_w Leverage_w Market_book_four_w Non_pension_CFO_w STD_CFO_w Board_Independence_w BoardSize_w Gender_Diversity_w Fund_Status_w FUNDING_RATIO_w Platn_Size_w CSR_Committee SustainabilityScore_w i.year i.ff_12 , robust cluster (id)
is there a way to solve this problem
im using simple probit model
where my independent variablecsopresence1 is dummy , my dependent variable hard_final_Exact_new is dummy and Moderator is dummy , which is calculated as Modriatr = csopresence1 * post_SFAS158 * PENADJ_indicator
and i have some control variable that is dummy post_SFAS158 PENADJ_indicator
the regression give me
note: Modriatr != 0 predicts failure perfectly;
Modriatr omitted and 8 obs not used.
this is my model
probit hard_final_Exact_new csopresence1 Modriatr post_SFAS158 PENADJ_indicator Firm_Size_w ROA_w Leverage_w Market_book_four_w Non_pension_CFO_w STD_CFO_w Board_Independence_w BoardSize_w Gender_Diversity_w Fund_Status_w FUNDING_RATIO_w Platn_Size_w CSR_Committee SustainabilityScore_w i.year i.ff_12 , robust cluster (id)
is there a way to solve this problem
HTML Code:
probit hard_final_Exact_new csopresence1 Modriatr post_SFAS158 PENADJ_indicator Firm_Size_w ROA_w Leverage_w Market_book_four_w Non_pension_CFO_w STD_CFO_w Board_Independe
> nce_w BoardSize_w Gender_Diversity_w Fund_Status_w FUNDING_RATIO_w Platn_Size_w CSR_Committee SustainabilityScore_w i.year i.ff_12 , robust cluster (id)
note: Modriatr != 0 predicts failure perfectly;
Modriatr omitted and 8 obs not used.
note: 2005.year != 0 predicts failure perfectly;
2005.year omitted and 165 obs not used.
note: 2006.year != 0 predicts failure perfectly;
2006.year omitted and 13 obs not used.
Iteration 0: Log pseudolikelihood = -358.35145
Iteration 1: Log pseudolikelihood = -329.04261
Iteration 2: Log pseudolikelihood = -326.56546
Iteration 3: Log pseudolikelihood = -326.52647
Iteration 4: Log pseudolikelihood = -326.52635
Iteration 5: Log pseudolikelihood = -326.52635
Probit regression Number of obs = 3,159
Wald chi2(44) = 84.81
Prob > chi2 = 0.0002
Log pseudolikelihood = -326.52635 Pseudo R2 = 0.0888
(Std. err. adjusted for 270 clusters in id)
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| Robust
hard_final_Exact_new | Coefficient std. err. z P>|z| [95% conf. interval]
----------------------+----------------------------------------------------------------
csopresence1 | .3049664 .1256368 2.43 0.015 .0587227 .55121
Modriatr | 0 (omitted)
post_SFAS158 | -.0068252 .384747 -0.02 0.986 -.7609154 .747265
PENADJ_indicator | -.0160293 .1407678 -0.11 0.909 -.2919292 .2598706
Firm_Size_w | -.1483298 .0811382 -1.83 0.068 -.3073578 .0106981
ROA_w | 2.188747 1.405676 1.56 0.119 -.5663282 4.943822
Leverage_w | -.7383755 .4308076 -1.71 0.087 -1.582743 .1059919
Market_book_four_w | -.0002983 .0091105 -0.03 0.974 -.0181546 .017558
Non_pension_CFO_w | -4.841646 1.890794 -2.56 0.010 -8.547535 -1.135757
STD_CFO_w | 1.102136 3.069188 0.36 0.720 -4.913361 7.117633
Board_Independence_w | .0038131 .005846 0.65 0.514 -.0076448 .015271
BoardSize_w | -.0198617 .026763 -0.74 0.458 -.0723163 .0325928
Gender_Diversity_w | -.0043376 .0068699 -0.63 0.528 -.0178023 .0091271
Fund_Status_w | -3.88352 2.021851 -1.92 0.055 -7.846275 .0792343
FUNDING_RATIO_w | -.5684102 .4264107 -1.33 0.183 -1.40416 .2673395
Platn_Size_w | .0334232 .0666827 0.50 0.616 -.0972726 .1641189
CSR_Committee | .1016902 .1371256 0.74 0.458 -.167071 .3704513
SustainabilityScore_w | .0028631 .00359 0.80 0.425 -.0041731 .0098993
|
year |
2005 | 0 (empty)
2006 | 0 (empty)
2007 | -.1937767 .6349589 -0.31 0.760 -1.438273 1.05072
2008 | .3669977 .5631947 0.65 0.515 -.7368436 1.470839
2009 | .5487587 .4103906 1.34 0.181 -.255592 1.353109
2010 | .391326 .4232784 0.92 0.355 -.4382844 1.220936
2011 | .0877705 .4469784 0.20 0.844 -.788291 .963832
2012 | .4454293 .4192494 1.06 0.288 -.3762845 1.267143
2013 | .3479672 .4203863 0.83 0.408 -.4759747 1.171909
2014 | .4517313 .422942 1.07 0.285 -.3772198 1.280682
2015 | .4870158 .426864 1.14 0.254 -.3496222 1.323654
2016 | .2188491 .4397017 0.50 0.619 -.6429504 1.080649
2017 | .3019857 .4428455 0.68 0.495 -.5659756 1.169947
2018 | .6105535 .4361753 1.40 0.162 -.2443342 1.465441
2019 | .2607304 .4637858 0.56 0.574 -.648273 1.169734
2020 | .6196371 .4507377 1.37 0.169 -.2637925 1.503067
2021 | .2225547 .4996484 0.45 0.656 -.7567382 1.201848
2022 | .5439303 .5910644 0.92 0.357 -.6145346 1.702395
|
ff_12 |
2 | .0279744 .4129966 0.07 0.946 -.781484 .8374329
3 | -.1731902 .2217855 -0.78 0.435 -.6078817 .2615013
4 | -.2119661 .4017845 -0.53 0.598 -.9994492 .575517
5 | -.1133531 .2490367 -0.46 0.649 -.6014561 .3747499
6 | .0049323 .2409315 0.02 0.984 -.4672847 .4771493
7 | .4607551 .3931313 1.17 0.241 -.309768 1.231278
8 | -.257131 .2631081 -0.98 0.328 -.7728133 .2585514
9 | .5244454 .2298999 2.28 0.023 .0738498 .9750409
10 | -.0693265 .2593176 -0.27 0.789 -.5775795 .4389266
11 | .4559202 .2359059 1.93 0.053 -.0064468 .9182872
12 | -.1601826 .2445231 -0.66 0.512 -.6394389 .3190738
|
_cons | -.7197823 .7756739 -0.93 0.353 -2.240075 .8005107
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