[Limdep Nlogit List] LIMDEP and Stata Yield very different results in Multivariate Probit
Fred Dzanku
fdzanku at gmail.com
Fri Mar 18 09:30:04 AEDT 2016
Dear Users, I estimated a trivariate probit model (with clustering) in
Limdep and Stata. Surprisingly, the results (particularly) the standard
errors are very different, yielding very different inference. In Stata I
used the mvprobit command (written by Lorenzo Cappellari and Stephen P.
Jenkins). The difference is indeed shocking. Does anyone know why this might
be the case?
The specification is:
y1 = f1(y2, y3, x1, x2, x3)
y2 = f2(x1, x2, x3, x4, x5)
y3 = f3(x2, x3, x4, x6)
In Limdep I wrote:
Sample ;all $
Skip $
Mprobit ;Lhs=y1,y2,y3
;eq1=y2,y3,x1,x2,x3,one
;eq2=x1,x2,x3,x4,x5,one
;eq3=x2,x3,x4,x6,one
;pts=200;cluster=vid $
And the results were:
Normal exit from iterations. Exit status=0.
+---------------------------------------------+
| Multivariate Probit Model: 3 equations. |
| Maximum Likelihood Estimates |
| Model estimated: Mar 17, 2016 at 00:29:05AM.|
| Dependent variable MVProbit |
| Weighting variable None |
| Number of observations 650 |
| Iterations completed 33 |
| Log likelihood function -1242.182 |
| Number of parameters 20 |
| Info. Criterion: AIC = 3.88364 |
| Finite Sample: AIC = 3.88569 |
| Info. Criterion: BIC = 4.02139 |
| Info. Criterion:HQIC = 3.93707 |
| Replications for simulated probs. = 200 |
+---------------------------------------------+
+---------------------------------------------------------------------+
| Covariance matrix for the model is adjusted for data clustering. |
| Sample of 650 observations contained 66 clusters defined by |
| variable VID which identifies by a value a cluster ID. |
| Sample of 650 observations contained 1 strata defined by |
| 650 observations (fixed number) in each stratum. |
+---------------------------------------------------------------------+
+--------+--------------+----------------+--------+--------+----------+
|Variable| Coefficient | Standard Error |b/St.Er.|P[|Z|>z]| Mean of X|
+--------+--------------+----------------+--------+--------+----------+
---------+Index function for Y1
Y2 | .84344025 5.74939391 .147 .8834 .38000000
Y3 | .42013852 3.25958960 .129 .8974 .35538462
X1 | -.00806300 .00860756 -.937 .3489 46.6107692
X2 | .31793308 .71794537 .443 .6579 .34307692
X3 | .01468727 .00840065 1.748 .0804 11.2224103
Constant| -.31411643 .77762634 -.404 .6863
---------+Index function for Y2
X1 | .00779023 .00104284 7.470 .0000 46.6107692
X2 | .16831606 .00775319 21.709 .0000 .34307692
X3 | -.00663025 .00072659 -9.125 .0000 11.2224103
X4 | .14847431 .14394530 1.031 .3023 .28923077
X5 | .04544672 .05689766 .799 .4244 .11538462
Constant| -.70426701 .08723116 -8.074 .0000
---------+Index function for Y3
X2 | .17303656 .01161279 14.901 .0000 .34307692
X3 | .00272569 .00050864 5.359 .0000 11.2224103
X4 | .19051601 .05574615 3.418 .0006 .28923077
X6 | .42854019 .04664493 9.187 .0000 .07692308
Constant| -.55390573 .03198934 -17.315 .0000
---------+Correlation coefficients
R(01,02)| -.58312487 3.20681595 -.182 .8557
R(01,03)| -.44115615 1.23356392 -.358 .7206
R(02,03)| .40497242 .00399374 101.402 .0000
In Stata I estimated:
mvprobit (y1 y2 y3 x1 x2 x3) (y2 x1 x2 x3 x4 x5) (y3 x2 x3 x4 x6),
cluster(vid) draw(200) nolog seed(1003)
with the following results:
Multivariate probit (MSL, # draws = 200) Number of obs =
650
Wald chi2(14) =
248.37
Log pseudolikelihood = -1241.6481 Prob > chi2 =
0.0000
(Std. Err. adjusted for 66 clusters in
vid)
----------------------------------------------------------------------------
--
| Robust
| Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
y1 |
y2 | 1.343666 .234929 5.72 0.000 .8832137
1.804118
y3 | -.465239 .7326024 -0.64 0.525 -1.901113
.9706353
x1 | -.00879 .0037455 -2.35 0.019 -.0161311
-.001449
x2 | .2877144 .1807015 1.59 0.111 -.0664541
.6418829
x3 | .0162344 .0078329 2.07 0.038 .0008822
.0315867
_cons | -.1612781 .3041286 -0.53 0.596 -.7573591
.4348029
-------------+--------------------------------------------------------------
--
y2 |
x1 | .0075814 .0042735 1.77 0.076 -.0007944
.0159573
x2 | .1548884 .0846257 1.83 0.067 -.0109749
.3207516
x3 | -.0071789 .0064509 -1.11 0.266 -.0198224
.0054645
x4 | .1732857 .0972146 1.78 0.075 -.0172514
.3638228
x5 | .0102722 .1315351 0.08 0.938 -.2475319
.2680762
_cons | -.6934089 .2054128 -3.38 0.001 -1.096011
-.2908072
-------------+--------------------------------------------------------------
--
y3 |
x2 | .1702344 .0881082 1.93 0.053 -.0024546
.3429234
x3 | .0032607 .0058996 0.55 0.580 -.0083022
.0148237
x4 | .1644255 .1287772 1.28 0.202 -.0879732
.4168241
x6 | .4566752 .2159169 2.12 0.034 .0334859
.8798645
_cons | -.554203 .1180065 -4.70 0.000 -.7854915
-.3229145
-------------+--------------------------------------------------------------
--
/atrho21 | -1.052254 .6801192 -1.55 0.122 -2.385263
.2807557
-------------+--------------------------------------------------------------
--
/atrho31 | .0235009 .4724996 0.05 0.960 -.9025812
.9495831
-------------+--------------------------------------------------------------
--
/atrho32 | .4372824 .0815021 5.37 0.000 .2775413
.5970236
-------------+--------------------------------------------------------------
--
rho21 | -.7826809 .2634854 -2.97 0.003 -.9831906
.2736043
-------------+--------------------------------------------------------------
--
rho31 | .0234966 .4722387 0.05 0.960 -.7175524
.7395942
-------------+--------------------------------------------------------------
--
rho32 | .4113893 .0677086 6.08 0.000 .270628
.5349282
----------------------------------------------------------------------------
--
Other programs in Stata such as gsem and cmp yield estimates that are closer
to the mvprobit estimates. What might be the problem here?
Fred
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