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Re: st: ereturn and sfcross
From
Federico Belotti <[email protected]>
To
[email protected]
Subject
Re: st: ereturn and sfcross
Date
Tue, 9 Jul 2013 14:21:42 +0200
My preferred approach is to use the the Programmer's utility for displaying
ancillary parameters -_diparm-. For a detailed explanation of its syntax see
-help _diparm-.
In your case, since the likelihood is parametrized with
sigma_u = exp(0.5 * Usigma)
sigma_v = exp(0.5 * Vsigma)
you can easily get what you're looking for (but in the -return- set of macros) using
webuse greene9
sfcross lnv lnk lnl
initial: Log likelihood = -37.029968
Iteration 0: Log likelihood = -37.029968 (not concave)
Iteration 1: Log likelihood = -18.020203 (not concave)
Iteration 2: Log likelihood = -7.6054699
Iteration 3: Log likelihood = 1.9182958
Iteration 4: Log likelihood = 2.8027655
Iteration 5: Log likelihood = 2.8604121
Iteration 6: Log likelihood = 2.8604897
Iteration 7: Log likelihood = 2.8604897
Stoc. frontier normal/exponential model Number of obs = 25
Wald chi2(2) = 845.68
Prob > chi2 = 0.0000
Log likelihood = 2.8605
------------------------------------------------------------------------------
lnv | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Frontier |
lnk | .2624859 .0919988 2.85 0.004 .0821717 .4428002
lnl | .7703795 .1109569 6.94 0.000 .5529079 .9878511
_cons | 2.069242 .2356159 8.78 0.000 1.607444 2.531041
-------------+----------------------------------------------------------------
Usigma |
_cons | -4.002457 .9274575 -4.32 0.000 -5.820241 -2.184674
-------------+----------------------------------------------------------------
Vsigma |
_cons | -3.527598 .4486176 -7.86 0.000 -4.406873 -2.648324
-------------+----------------------------------------------------------------
sigma_u | .1351691 .0626818 2.16 0.031 .0544692 .3354317
sigma_v | .1713925 .0384448 4.46 0.000 .1104231 .2660258
lambda | .7886525 .087684 8.99 0.000 .616795 .9605101
------------------------------------------------------------------------------
_diparm Usigma, func( exp(0.5*@) ) der( 0.5*exp(0.5*@) ) level(95) label(sigma_u) prob
sigma_u | .1351691 .0626818 2.16 0.031 .0544692 .3354317
return li
scalars:
r(p) = .0310498704827077
r(z) = 2.156432996353714
r(i) = 0
r(ub) = .3354316987258648
r(lb) = .0544691789417937
r(est) = .1351691134122325
r(se) = .062681805389172
macros:
r(prob) : "prob"
_diparm Vsigma, func( exp(0.5*@) ) der( 0.5*exp(0.5*@) ) level(95) label(sigma_v) prob
sigma_v | .1713925 .0384448 4.46 0.000 .1104231 .2660258
return li
scalars:
r(p) = 8.26741242191e-06
r(z) = 4.458139422112636
r(i) = 0
r(ub) = .2660258073343608
r(lb) = .110423050288458
r(est) = .1713924767932063
r(se) = .0384448444889564
macros:
r(prob) : "prob"
_diparm Vsigma Usigma, level(95) func( sqrt(exp(@2-@1))) der( -0.5*exp(0.5*@1) 0.5*exp(0.5*@2) ) label(lambda) prob
lambda | .7886525 .087684 8.99 0.000 .616795 .9605101
return li
scalars:
r(p) = 2.37835973667e-19
r(z) = 8.99425674924732
r(i) = 0
r(ub) = .9605100595097473
r(lb) = .6167950351044211
r(se) = .0876840153993916
r(est) = .7886525473070842
macros:
r(prob) : "prob"
HTH,
Federico
On Jul 9, 2013, at 1:25 PM, Paulo Regis wrote:
> Dear all,
>
> I am using the new command -sfcross and I would like to know if
> someone with some experience with this command could tell me how to
> obtain some of the estimated parameters for further manipulation since
> it seems they are not available.
>
> Below, you can see an example. I am interested in sigma_u,sigma_v and
> lambda. If you check "ereturn list" after the command, you can see
> their standard errors are not available. How can I obtain them? For
> example, I would like to have them available t be used with -outreg
> or outreg2.
>
> Kind Regards
>
> Paulo
>
> Example:
>
> . webuse greene9
>
> . sfcross lnv lnk lnl
>
>
> initial: Log likelihood = -37.029968
> Iteration 0: Log likelihood = -37.029968 (not concave)
> Iteration 1: Log likelihood = -18.020203 (not concave)
> Iteration 2: Log likelihood = -7.6054699
> Iteration 3: Log likelihood = 1.9182958
> Iteration 4: Log likelihood = 2.8027655
> Iteration 5: Log likelihood = 2.8604121
> Iteration 6: Log likelihood = 2.8604897
> Iteration 7: Log likelihood = 2.8604897
>
> Stoc. frontier normal/exponential model Number of obs = 25
>
> Wald chi2(2) = 845.68
> Prob > chi2 = 0.0000
>
> Log likelihood = 2.8605
> ------------------------------------------------------------------------------
> lnv | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> Frontier |
> lnk | .2624859 .0919988 2.85 0.004 .0821717 .4428002
> lnl | .7703795 .1109569 6.94 0.000 .5529079 .9878511
> _cons | 2.069242 .2356159 8.78 0.000 1.607444 2.531041
> -------------+----------------------------------------------------------------
> Usigma |
> _cons | -4.002457 .9274575 -4.32 0.000 -5.820241 -2.184674
> -------------+----------------------------------------------------------------
> Vsigma |
> _cons | -3.527598 .4486176 -7.86 0.000 -4.406873 -2.648324
> -------------+----------------------------------------------------------------
> sigma_u | .1351691 .0626818 2.16 0.031 .0544692 .3354317
> sigma_v | .1713925 .0384448 4.46 0.000 .1104231 .2660258
> lambda | .7886525 .087684 8.99 0.000 .616795 .9605101
> ------------------------------------------------------------------------------
>
> . ereturn list
>
> scalars:
> e(rank) = 5
> e(N) = 25
> e(ic) = 7
> e(k) = 5
> e(k_eq) = 3
> e(k_dv) = 1
> e(converged) = 1
> e(rc) = 0
> e(k_autoCns) = 0
> e(ll) = 2.860489723076469
> e(iterations) = 8
> e(chi2) = 845.6810351779072
> e(p) = 2.3051356429e-184
> e(df_m) = 2
> e(z) = -.634477395928109
> e(p_z) = .2628846569875348
> e(sigma_u) = .1351691134122321
> e(sigma_v) = .1713924767932067
> e(lambda) = .7886525473070807
>
> macros:
> e(cmdline) : "sfcross lnv lnk lnl"
> e(covariates) : "lnk lnl _cons"
> e(cilevel) : "95"
> e(marginsok) : "default xb"
> e(title) : "Stoc. frontier normal/exponential model"
> e(crittype) : "Log likelihood"
> e(dist) : "exponential"
> e(function) : "production"
> e(depvar) : "lnv"
> e(cmd) : "sfcross"
> e(predict) : "sfcross_p"
> e(opt) : "moptimize"
> e(user) : "_cross_exp()"
> e(ml_method) : "lf2"
> e(singularHmethod) : "m-marquardt"
> e(technique) : "nr"
> e(which) : "max"
> e(properties) : "b V"
>
> matrices:
> e(b) : 1 x 5
> e(V) : 5 x 5
> e(ilog) : 1 x 20
> e(gradient) : 1 x 5
>
> functions:
> e(sample)
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/faqs/resources/statalist-faq/
> * http://www.ats.ucla.edu/stat/stata/
--
Federico Belotti, PhD
Research Fellow
Centre for Economics and International Studies
University of Rome Tor Vergata
tel/fax: +39 06 7259 5627
e-mail: [email protected]
web: http://www.econometrics.it
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/