[Limdep Nlogit List] LIMDEP and Stata Yield very different results in Multivariate Probit
Fred Dzanku
fdzanku at gmail.com
Fri Mar 18 10:20:35 AEDT 2016
Thanks a lot Steven, I know that the Stata also used MSL. All check the
other issues but the results are too different to believe.
Fred
On 17 Mar 2016 23:06, "Steven Yen" <syen04 at gmail.com> wrote:
> I would examine the gradient vector at your estimates, in both Stata
> and Limdep. In Limdep, this can be done by ;output=2
> Your estimates are very different and not just standard errors. At ML
> estimates, gradients are supposed to be very small, e.g., 1e-6.
> Also, Limdep may be doing maximum simulated likelihood. Find out if
> this is what's done in Stata. Results from simulation and quadrature
> and simulations are likely to differ, slightly.
>
> On Thu, Mar 17, 2016 at 6:30 PM, Fred Dzanku <fdzanku at gmail.com> wrote:
> > 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
> >
> >
> >
> >
> > _______________________________________________
> > Limdep site list
> > Limdep at limdep.itls.usyd.edu.au
> > http://limdep.itls.usyd.edu.au
> _______________________________________________
> Limdep site list
> Limdep at limdep.itls.usyd.edu.au
> http://limdep.itls.usyd.edu.au
>
More information about the Limdep
mailing list