Hi Mark:
Thanks for your prompt response to my questions. I am reposing three of the earlier questions, which you needed more information. I have now provided enough information below to enable you to help me.
1. Is there anything with my syntax below? If yes, how can it be corrected?
ivprobit vrics mhol acmt iacm bdze mdir dual ihol aud_1 aud_2 aud_3 aud_4 size perf levg ind_1 ind_2 ind_3 ind_4 ind_5 ind_6 ind_7 ind_8 grth efin optn (bdin = dual ihol mhol fcash size levg ind_1 ind_2 ind_3 ind_4 ind_5 ind_6 ind_7 ind_8 optn vrics_1), first
2. Stata increases the number of the instrumented variables in its results than I originally specified in the syntax? Why that? Is it because my model is under-identified?
3. For the fitting of the full model (i.e., probit model with the endogenous regressor), Stata goes through iteration from 1 to 1070, reporting that the intervening iterations (i.e., 1 to 1068) are not concave. Why this long iteration? Does it suggest that the model is mis-specified? Or implies that the results are not correct? I DON’T HAVE MULTICOLLINEARITY PROBLEM IN THE DATA.
The following are the equations I have been estimating with the ivprobit:
BDIN, = F(δ0 + δ1MHOL + δ2BDZE + δ3DUAL + δ4IHOL + δ5SIZE + δ6LEVG
+ δ7GRTH + + δ17EFIN + δ18OPTN + δ19VRICSt_1) (1)
Prob (VRICS = 1) = F(β0 + β1MHOL + β2ACMT + β3IACM + β4BDZE
+ β5FT_BDIN + β6MDIR + β7DUAL + βb8IHOL + + β14SIZE
+ β15PERF + β16LEVG + + β26GRTH + β27EFIN + β28OPTN) (2)
Equation 2 is the model of interest. FT_BDIN in equation 2 is the fitted values of BDIN in equation 1. My sample size is 198 companies (110 experimental and 88 control sub-sample).
The syntax and results are as follows:
ivprobit vrics mhol acmt iacm bdze mdir dual ihol aud_1 aud_2 aud_3 aud_4 size perf levg ind_1 ind_2 ind_3 ind_4 ind_5 ind_6 ind_7 ind_8 grth efin optn (bdin = dual ihol mhol efin size levg ind_1 ind_2 ind_3 ind_4 ind_5 ind_6 ind_7 ind_8 optn vrics_1), first
Fitting exogenous probit model
Iteration 0: log likelihood = -136.01839
.
Iteration 6: log likelihood = -103.21347
Fitting full model
Iteration 0: log likelihood = -507.53953 (not concave)
Iteration 1: log likelihood = -507.44146 (not concave)
.
.
Iteration 1061:log likelihood = -467.50251 (backed up)
Iteration 1062:log likelihood = -465.17425
Iteration 1063:log likelihood = -462.61643
.
.
Iteration 1070:log likelihood = -462.0811
Probit model with endogenous regressors Number of obs = 198
Wald chi2(26) = 392.55
Log likelihood = -462.0811 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
vrics |
bdin | .535787 .0270656 19.80 0.000 .4827393 .5888347
mhol | .0082955 .6629267 0.01 0.990 -1.291017 1.307608
acmt | .0173609 .0293927 0.59 0.555 -.0402477 .0749695
iacm | -.2845267 .2214701 -1.28 0.199 -.7186002 .1495468
bdze | .1125746 .0368413 3.06 0.002 .040367 .1847823
mdir | -.1984201 .0348804 -5.69 0.000 -.2667844 -.1300558
dual | .3046553 .1763825 1.73 0.084 -.041048 .6503587
ihol | .5343345 .3381111 1.58 0.114 -.1283511 1.19702
aud_1 | -.118587 .2618686 -0.45 0.651 -.63184 .3946661
aud_2 | -.1315854 .1602961 -0.82 0.412 -.4457599 .1825892
aud_3 | -.2391904 .3023197 -0.79 0.429 -.8317261 .3533454
aud_4 | -.1764671 .2734199 -0.65 0.519 -.7123601 .359426
size | -.394326 .085888 -4.59 0.000 -.5626634 -.2259885
perf | .1184288 .5215651 0.23 0.820 -.90382 1.140678
levg | -.6340103 .4221428 -1.50 0.133 -1.461395 .1933744
ind_1 | .638568 .4017463 1.59 0.112 -.1488403 1.425976
ind_2 | .5798336 .3313281 1.75 0.080 -.0695576 1.229225
ind_3 | .7170456 .3188755 2.25 0.025 .0920611 1.34203
ind_4 | .3894297 .3882769 1.00 0.316 -.371579 1.150438
ind_5 | .7477128 .3261907 2.29 0.022 .1083907 1.387035
ind_6 | .8033681 .4126983 1.95 0.052 -.0055056 1.612242
ind_7 | .5320546 .4018902 1.32 0.186 -.2556357 1.319745
ind_8 | 1.585647 .5801551 2.73 0.006 .4485636 2.72273
grth | .0025123 .0013356 1.88 0.060 -.0001053 .0051299
efin | .0080272 .0116433 0.69 0.491 -.0147932 .0308476
optn | .0069244 .011954 0.58 0.562 -.0165049 .0303537
_cons | 7.2861 1.909368 3.82 0.000 3.543807 11.02839
-------------+----------------------------------------------------------------
bdin |
mhol | .0378184 1.235286 0.03 0.976 -2.383298 2.458935
acmt | -.033273 .0544709 -0.61 0.541 -.1400339 .073488
iacm | .596355 .367546 1.62 0.105 -.1240219 1.316732
bdze | -.2195357 .0680645 -3.23 0.001 -.3529396 -.0861318
mdir | .3700483 .0622494 5.94 0.000 .2480418 .4920548
dual | -.573009 .326602 -1.75 0.079 -1.213137 .0671192
ihol | -1.012138 .6303383 -1.61 0.108 -2.247578 .2233028
aud_1 | .14425 .4679218 0.31 0.758 -.7728599 1.06136
aud_2 | .1355325 .236811 0.57 0.567 -.3286086 .5996736
aud_3 | .300847 .4963293 0.61 0.544 -.6719404 1.273634
aud_4 | .2759295 .4846338 0.57 0.569 -.6739352 1.225794
size | .7537389 .1458796 5.17 0.000 .4678201 1.039658
perf | -.181126 .9590768 -0.19 0.850 -2.060882 1.69863
levg | 1.132908 .7862537 1.44 0.150 -.4081206 2.673937
ind_1 | -1.167724 .744777 -1.57 0.117 -2.627461 .2920118
ind_2 | -1.046297 .6184434 -1.69 0.091 -2.258424 .1658298
ind_3 | -1.318878 .590602 -2.23 0.026 -2.476436 -.1613189
ind_4 | -.7132557 .721654 -0.99 0.323 -2.127672 .7011601
ind_5 | -1.400158 .6024688 -2.32 0.020 -2.580976 -.2193413
ind_6 | -1.427494 .7589549 -1.88 0.060 -2.915018 .0600307
ind_7 | -1.033129 .7444047 -1.39 0.165 -2.492135 .4258778
ind_8 | -2.996058 1.066981 -2.81 0.005 -5.087302 -.9048135
grth | -.0046962 .0024757 -1.90 0.058 -.0095485 .000156
efin | -.0175522 .0208806 -0.84 0.401 -.0584774 .0233729
optn | -.0136099 .0222878 -0.61 0.541 -.0572931 .0300734
vrics_1 | .198787 .3208153 0.62 0.536 -.4299995 .8275734
_cons | -13.92576 3.327674 -4.18 0.000 -20.44788 -7.403639
-------------+----------------------------------------------------------------
/athrho | -3.964625 1.619449 -2.45 0.014 -7.138687 -.7905633
/lnsigma | .6231125 .050253 12.40 0.000 .5246184 .7216065
-------------+----------------------------------------------------------------
rho | -.9992801 .0023307 -.9999987 -.658728
sigma | 1.864723 .0937079 1.689814 2.057736
------------------------------------------------------------------------------
Instrumented: bdin
Instruments: mhol acmt iacm bdze mdir dual ihol aud_1 aud_2 aud_3 aud_4 size
perf levg ind_1 ind_2 ind_3 ind_4 ind_5 ind_6 ind_7 ind_8 grth
efin optn vrics_1*
------------------------------------------------------------------------------
Wald test of exogeneity (/athrho = 0): chi2(1) = 5.99 Prob > chi2 = 0.0144
*Some of the instrumented variables were not specified in the original syntax.
Below is the syntax and results when I used two-step estimator. What does the note at the bottom of the results mean?
ivprobit vrics mhol acmt iacm bdze mdir dual ihol aud_1 aud_2 aud_3 aud_4 size perf levg ind_1 ind_2 ind_3 ind_4 ind_5 ind_6 ind_7 ind_8 grth efin optn (bdin = dual ihol mhol efin size levg ind_1 ind_2 ind_3 ind_4 ind_5 ind_6 ind_7 ind _8 optn vrics_1), twostep first
Checking reduced-form model
First stage regression
Source | SS df MS Number of obs = 198
-------------+------------------------------ F( 26, 171) = 5.18
Model | 542.099097 26 20.8499653 Prob > F = 0.0000
Residual | 688.483679 171 4.02622035 R-squared = 0.4405
-------------+------------------------------ Adj R-squared = 0.3555
Total | 1230.58278 197 6.24661308 Root MSE = 2.0065
------------------------------------------------------------------------------
bdin | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
vrics_1 | .1988048 .3453415 0.58 0.566 -.4828765 .8804861
mhol | .0378225 1.329227 0.03 0.977 -2.585984 2.661629
acmt | -.0332726 .0586135 -0.57 0.571 -.1489718 .0824266
iacm | .5963493 .3955089 1.51 0.133 -.1843592 1.377058
bdze | -.2195362 .0732413 -3.00 0.003 -.3641098 -.0749626
mdir | .3700488 .0669836 5.52 0.000 .2378275 .50227
dual | -.5730081 .3514396 -1.63 0.105 -1.266727 .1207105
ihol | -1.012142 .6782774 -1.49 0.137 -2.351017 .3267326
aud_1 | .14425 .5035061 0.29 0.775 -.8496378 1.138138
aud_2 | .1355322 .2548215 0.53 0.596 -.3674686 .638533
aud_3 | .3008486 .5340752 0.56 0.574 -.7533807 1.355078
aud_4 | .2759339 .5214952 0.53 0.597 -.7534632 1.305331
size | .7537372 .1569757 4.80 0.000 .4438776 1.063597
perf | -.1811319 1.032013 -0.18 0.861 -2.218258 1.855994
levg | 1.132905 .8460482 1.34 0.182 -.5371384 2.802948
ind_1 | -1.167724 .801416 -1.46 0.147 -2.749666 .4142183
ind_2 | -1.046293 .6654784 -1.57 0.118 -2.359903 .2673175
ind_3 | -1.318876 .6355171 -2.08 0.039 -2.573345 -.0644069
ind_4 | -.7132544 .7765309 -0.92 0.360 -2.246075 .8195664
ind_5 | -1.400157 .6482868 -2.16 0.032 -2.679832 -.1204813
ind_6 | -1.427492 .8166718 -1.75 0.082 -3.039548 .1845637
ind_7 | -1.033129 .8010162 -1.29 0.199 -2.614282 .5480241
ind_8 | -2.996057 1.148121 -2.61 0.010 -5.262373 -.7297423
grth | -.0046962 .002664 -1.76 0.080 -.0099548 .0005623
efin | -.0175522 .0224685 -0.78 0.436 -.0619036 .0267992
optn | -.0136099 .0239828 -0.57 0.571 -.0609505 .0337306
_cons | -13.92572 3.580775 -3.89 0.000 -20.99394 -6.857512
------------------------------------------------------------------------------
Two-step probit with endogenous regressors Number of obs = 198
Wald chi2(26) = 0.99
Prob > chi2 = 1.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
bdin | 14.12199 24.61414 0.57 0.566 -34.12084 62.36482
mhol | .2186578 18.79424 0.01 0.991 -36.61738 37.05469
acmt | .4575839 1.235632 0.37 0.711 -1.96421 2.879378
iacm | -7.49924 17.15531 -0.44 0.662 -41.12302 26.12454
bdze | 2.967177 5.363456 0.55 0.580 -7.545004 13.47936
mdir | -5.22986 9.026449 -0.58 0.562 -22.92138 12.46166
dual | 8.029926 15.17887 0.53 0.597 -21.72012 37.77997
ihol | 14.08375 25.45977 0.55 0.580 -35.81648 63.98398
aud_1 | -3.125751 7.994754 -0.39 0.696 -18.79518 12.54368
aud_2 | -3.468392 5.087113 -0.68 0.495 -13.43895 6.502165
aud_3 | -6.304659 10.32934 -0.61 0.542 -26.54979 13.94047
aud_4 | -4.651355 9.189619 -0.51 0.613 -22.66268 13.35997
size | -10.39339 19.15959 -0.54 0.587 -47.9455 27.15873
perf | 3.121618 14.83004 0.21 0.833 -25.94473 32.18797
levg | -16.71092 31.29799 -0.53 0.593 -78.05385 44.63201
ind_1 | 16.83106 30.99911 0.54 0.587 -43.92608 77.58821
ind_2 | 15.28294 28.46859 0.54 0.591 -40.51448 71.08035
ind_3 | 18.89951 34.12934 0.55 0.580 -47.99277 85.79179
ind_4 | 10.26438 21.0224 0.49 0.625 -30.93877 51.46753
ind_5 | 19.70779 36.16063 0.55 0.586 -51.16575 90.58132
ind_6 | 21.17483 37.29913 0.57 0.570 -51.93013 94.27978
ind_7 | 14.02357 27.7139 0.51 0.613 -40.29468 68.34181
ind_8 | 41.79358 75.57157 0.55 0.580 -106.324 189.9111
grth | .0662178 .1233329 0.54 0.591 -.1755102 .3079459
efin | .2115723 .5480824 0.39 0.699 -.8626495 1.285794
optn | .1825097 .4637365 0.39 0.694 -.7263971 1.091416
_cons | 192.0422 356.4177 0.54 0.590 -506.5236 890.6081
------------------------------------------------------------------------------
Instrumented: bdin
Instruments: mhol acmt iacm bdze mdir dual ihol aud_1 aud_2 aud_3 aud_4
size perf levg ind_1 ind_2 ind_3 ind_4 ind_5 ind_6 ind_7
ind_8 grth efin optn vrics_1
------------------------------------------------------------------------------
Wald test of exogeneity: chi2(1) = 51.79 Prob > chi2 = 0.0000
note: 47 failures and 30 successes completely determined.
Regards,
Stephen
---- "Schaffer wrote:
> Stephen,
>
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]] On Behalf Of
> > [email protected]
> > Sent: 06 January 2008 21:45
> > To: [email protected]
> > Subject: st: Questions on ivprobit (probit model with an
> > endogenous regressor)
> >
> > Dear colleagues:
> >
> > I need your help with respect to the following questions
> > about ivprobit (command for probit model with an endogenous
> > regressor):
> >
> > 1. Is there anything with my syntax below? If yes, how can it
> > be corrected?
> >
> > ivprobit vrics mhol acmt iacm bdze mdir dual ihol aud_1 aud_2
> > aud_3 aud_4 size perf levg ind_1 ind_2 ind_3 ind_4 ind_5
> > ind_6 ind_7 ind_8 rev_gwth fcash optns (bdin= dual ihol
> > mhol fcash size levg ind_1 ind_2 ind_3 ind_4 ind_5 ind_6
> > ind_7 ind_8 optns vrics_1), first
>
> It's impossible to tell without seeing the actual call to -ivprobit- and
> what Stata makes of it. You should post this.
>
> > 2. The dependent variable, vrics, is a dummy coded 1/0.
> > Hence, my use of ivprobit. However, the endogenous regressor,
> > bdin, is not. Isn't ivprobit reading the data on bdin as
> > dummy?
>
> No. It's because you're using the default ML estimator. If you use the
> two-step estimator, you'll see that the first-step estimates for bdin
> are exactly the same estimates you get if you use -regress-.
>
> > My question is based on the fact that OLS estimates of
> > the bdin equation is different from those returned by
> > ivprobit for the first-stage regression. Also, Stata 10
> > reports iteration for "Fitting exogenous probit model".
> >
> > 3. Stata increases the number of the instrumented variables
> > in its results than I originally specified in the syntax? Why
> > that? Is it because my model is under-identified?
>
> Again, it's impossible to tell unless you show us the call to -ivprobit-
> and the results.
>
> > 4. For the fitting of the full model (i.e., probit model with
> > the endogenous regressor), Stata goes through iteration from
> > 1 to 1070, reporting that the intervening iterations (i.e., 1
> > to 1068) are not concave. Why this long iteration? Does it
> > suggest that the model is mis-specified? Or implies that the
> > results are not correct?
>
> You're probably asking a lot of the data, maybe too much. Perhaps you
> have some multicollinearity problems. Are many of the coefficients
> insignificant?
>
> > 5. Stata 10 does not report model summary statistics for the
> > first-stage regression with bdin as the dependent variable.
> > Is there any way of getting these statistics?
>
> With the ML estimator, the "first-stage regression" isn't really a first
> stage, since it's estimated simultaneously with the main equation. I
> think this means that you just have to get the stats you want from the
> main -ivprobit- results with the ML estimator. You could switch to the
> two-step estimator so that the first-stage results are reproducible with
> a simple call to -regress-, but this doesn't seem like a good reason to
> do this.
>
> Hope this helps.
>
> Cheers,
> Mark
>
>
> Prof. Mark Schaffer
> Director, CERT
> Department of Economics
> School of Management & Languages
> Heriot-Watt University, Edinburgh EH14 4AS
> tel +44-131-451-3494 / fax +44-131-451-3296
> email: [email protected]
> web: http://www.sml.hw.ac.uk/ecomes
>
> > I look forward to hearing from you. Thanks for your cooperation.
> >
> > Regards,
> >
> > Stephen
> > --
> > Stephen Owusu-Ansah, PhD, CIA, CBM
> >
> > *
> > * For searches and help try:
> > * http://www.stata.com/support/faqs/res/findit.html
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
> >
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
