Hello everyone,
I'm using Stata 13 and trying to interpret the results below the text. I'm using a system of seemingly unrelated bivariate probit models to figure out if the correlation between the error terms of the two equations differs from zero to control for selection bias. I assume if rho is insignificantly different from zero then there would be no selection bias.
Now I'm having a hard time to interpret the results at the bottom:
Does this mean that rho is significantly (10% level) different from zero? And as a reason that there is selection bias?
Here is the whole output:
Best regards and thanks in advance,
Tobias
I'm using Stata 13 and trying to interpret the results below the text. I'm using a system of seemingly unrelated bivariate probit models to figure out if the correlation between the error terms of the two equations differs from zero to control for selection bias. I assume if rho is insignificantly different from zero then there would be no selection bias.
Now I'm having a hard time to interpret the results at the bottom:
Code:
rho | .1780497 .0912844 -.0048039 .3493819 ------------------------------------------------------------------------------ Likelihood-ratio test of rho=0: chi2(1) = 3.73049 Prob > chi2 = 0.0534
Here is the whole output:
Code:
biprobit (valmethod = risk size boldness recom growth profit dax_past time_1 time_2 time_3 time_4 time_5 time_6 time_7 time_8 time_9 time_10 time_11 broker_1 broker_2 broker_3 broker_4 industry_1 indust > ry_2 industry_3 industry_4 industry_5 industry_6 industry_7 industry_8) (met_end = valmethod risk size boldness recom growth profit dax_future) Fitting comparison equation 1: Iteration 0: log likelihood = -1274.8859 Iteration 1: log likelihood = -968.87038 Iteration 2: log likelihood = -959.25266 Iteration 3: log likelihood = -957.87107 Iteration 4: log likelihood = -957.7174 Iteration 5: log likelihood = -957.69015 Iteration 6: log likelihood = -957.68504 Iteration 7: log likelihood = -957.68402 Iteration 8: log likelihood = -957.6838 Iteration 9: log likelihood = -957.68375 Iteration 10: log likelihood = -957.68375 Fitting comparison equation 2: Iteration 0: log likelihood = -1262.3575 Iteration 1: log likelihood = -1113.7912 Iteration 2: log likelihood = -1110.6841 Iteration 3: log likelihood = -1110.6833 Iteration 4: log likelihood = -1110.6833 Comparison: log likelihood = -2068.3671 Fitting full model: Iteration 0: log likelihood = -2068.3671 Iteration 1: log likelihood = -2066.5212 Iteration 2: log likelihood = -2066.5018 Iteration 3: log likelihood = -2066.5018 Seemingly unrelated bivariate probit Number of obs = 1930 Wald chi2(38) = 617.11 Log likelihood = -2066.5018 Prob > chi2 = 0.0000 Coef. Std. Err. z P>z [95% Conf. Interval] valmethod risk -.3115484 .0820794 -3.80 0.000 -.4724211 -.1506756 size -.1547326 .0450794 -3.43 0.001 -.2430866 -.0663786 boldness -.0038907 .0031274 -1.24 0.213 -.0100202 .0022389 recom .0665691 .0813913 0.82 0.413 -.0929549 .2260931 growth .0093777 .0039054 2.40 0.016 .0017234 .0170321 profit .4187254 .1786379 2.34 0.019 .0686015 .7688493 dax_past -.0001211 .0029369 -0.04 0.967 -.0058773 .0056351 time_1 -.8219447 .1892348 -4.34 0.000 -1.192838 -.4510512 time_2 -.6236781 .2084041 -2.99 0.003 -1.032143 -.2152137 time_3 -.3244276 .1969392 -1.65 0.099 -.7104213 .0615661 time_4 -.623266 .1832377 -3.40 0.001 -.9824052 -.2641268 time_5 -.291307 .2049938 -1.42 0.155 -.6930875 .1104736 time_6 -.5059805 .2807058 -1.80 0.071 -1.056154 .0441927 time_7 -.4818854 .1752289 -2.75 0.006 -.8253276 -.1384431 time_8 -.4695556 .1623892 -2.89 0.004 -.7878326 -.1512787 time_9 -.221224 .1807373 -1.22 0.221 -.5754625 .1330146 time_10 -.2304714 .1762917 -1.31 0.191 -.5759967 .115054 time_11 -.1911421 .1783841 -1.07 0.284 -.5407685 .1584842 broker_1 -1.121648 .1262826 -8.88 0.000 -1.369157 -.8741384 broker_2 -.5694673 .0908427 -6.27 0.000 -.7475158 -.3914189 broker_3 -.955237 .0935743 -10.21 0.000 -1.138639 -.7718347 broker_4 -.1774212 .1414599 -1.25 0.210 -.4546774 .099835 industry_1 1.275041 .1992633 6.40 0.000 .884492 1.66559 industry_2 .5504341 .1857927 2.96 0.003 .1862871 .914581 industry_3 -.2467123 .2391105 -1.03 0.302 -.7153603 .2219357 industry_4 -.1052097 .1917304 -0.55 0.583 -.4809945 .270575 industry_5 .0450062 .2149748 0.21 0.834 -.3763366 .466349 industry_6 -.139522 .2012488 -0.69 0.488 -.5339624 .2549183 industry_7 .4166587 .1980507 2.10 0.035 .0284865 .8048309 industry_8 7.671736 1384.863 0.01 0.996 -2706.609 2721.953 _cons 1.84124 .5802919 3.17 0.002 .7038887 2.978591 met_end valmethod -.2850019 .1310774 -2.17 0.030 -.5419088 -.028095 risk .0454646 .0530483 0.86 0.391 -.0585082 .1494374 size -.0357795 .0373738 -0.96 0.338 -.1090309 .0374718 boldness -.0304921 .0032121 -9.49 0.000 -.0367877 -.0241964 recom .2089467 .0733589 2.85 0.004 .0651658 .3527275 growth .0095815 .0034149 2.81 0.005 .0028884 .0162746 profit -.0439051 .1278892 -0.34 0.731 -.2945634 .2067531 dax_future .0245759 .001917 12.82 0.000 .0208186 .0283333 _cons .0425873 .4349468 0.10 0.922 -.8098927 .8950673 /athrho .1799678 .094273 1.91 0.056 -.004804 .3647395 rho .1780497 .0912844 -.0048039 .3493819 Likelihood-ratio test of rho=0: chi2(1) = 3.73049 Prob > chi2 = 0.0534
Tobias
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