[Limdep Nlogit List] LIMDEP and Stata Yield very different results in Multivariate Probit
Steven Yen
syen04 at gmail.com
Fri Mar 18 10:05:28 AEDT 2016
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
>
>
>
>
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