I am running a series of loops that try out all combinations of Stata's new lasso functions dsregress, poregress and xporegress, and export the output using esttab. However, several of the models do not reach lambda convergence. When that happens, especially when the tuning parameter is "cv". When that happens, I get error code r430 and the loop stops. If I use "capture noisily..." for the regression command, then there are no estimates to store. If I also use "capture noisily..." for storing the estimates, then esttab uses the estimates stored for the previous regression.

What I would like is for the columns of the regressions that do not converge to have a dot (".") for the stored estimates (e.g. e(b), e(se), e(N) and e(k_controls_sel), etc.), so that I can see precisely which columns didn't converge. I thought the solution would be to manually create and store these estimates if the regression spits out an error (-rc == 430). But how can I manually do this? Or is there a better way?

I've attached some dataex but I couldn't include enough variables into dataex so that you can reproduce the lack of convergence (so you'll have to take my word for it...). Here is my code, where the extra variables are stored in $econ, $demog and $politics:

Code:
estimates     clear
forvalues i = 2013(1)2013 {
    foreach var in GII {
        local     controls_`var'     $econ $demog $politics c.`var'#c.($econ $demog )
        foreach method in ds po xpo  {
            foreach select in plugin cv adaptive {
                `method'regress    G_STEMfprop `var' if year==`i', ///
                controls(`controls_`var'') resample(1) xfolds(5) selection(`select') rseed(64)
                if        _rc != 0 {
                    local conv "NO"
                }
                else {
                    local conv "YES"
                }
                estimates store GSTEM`i'`var'_`method'_`select'
                if         "`select'" == "plugin" {
                    estadd local Tuning "\textit{plug}", replace
                }
                else if    "`select'" == "cv" { 
                    estadd local Tuning "\textit{cv}", replace
                }
                else if "`select'" == "adaptive" { 
                    estadd local Tuning "\textit{adap}", replace
                }
                else {
                    estadd local Tuning "\textit{NULL}", replace
                }
                /// estadd local Tuning "\textit{`select'}", replace
                estadd local Estimator "\textit{`method'}", replace
                estadd local Convergence "`conv'", replace
            }
        }    
    }
    esttab GSTEM`i'*    using "../Tex/Lasso/ML`i'.tex",  replace unstack  ///
        scalars(Estimator Tuning "Convergence Convergence?" ///
        "k_controls_sel Selected controls") ///
        b(%9.2f) se(%9.2f) sfmt(%9.0f) ///
        rename(GII equality WBL equality)  brackets ///
        mgroups(GII WBL, pattern(1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0)                   ///
        prefix(\multicolumn{@span}{c}{) suffix(})   ///
        span erepeat(\cmidrule(lr){@span}))  ///
        nomtitles nogaps nodep nonotes
}
Data:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(GII G_STEMfprop HDI_L1 GNIpc_L1 GNIpcratio_L1 HDI_f_L1 HDI_m_L1 HDIratio_L1 unempratio_L1 unemp_L1 unempyouth_L1 unempyouthratio_L1)
  1.350119    -.530056 1.6468107   1.495874   .5421427  1.447422 1.5387285  .58638203  -.27793115  -.4317768   -.4028556  -.3991656
  1.484906   -.9263493 1.4719187   1.929531 -.28471923  1.217569 1.3541586   .4099647   -.3242405  -.4910843   -.5917258 -.31485155
  1.583404  -1.2777967 1.5020726   1.708658  .27621728 1.2976046 1.3958483   .5170524   -.3253803 -.04883888    .2596202  -.3606904
  .8420768   .15717806   .790443 .067976505  .50633484   .671056  .5332401   .7813587    -.441689   .7322357    .9411384  -.4013202
  1.350119  -.38309535 1.4598573  1.5070184   .5229612  1.308491 1.2970958   .7415117   -.3841619 -.09013935   -.1771267  -.4866935
  .3651392  -1.6460524  .9472426  .25448307  -.3222311  .7663235  .8046443   .4235463 .0035964234 -.19471213  .016401809  .01232764
 1.2723576 -.012733627 1.1764117   .9767638    .565453  1.004694 1.0235436   .5649824   -.3822291   .6537647     .807025   -.346941
  1.251621   -.5880356  1.266873   .7851835  .09969018 1.1027188 1.0995001    .649528  -.05063437 -.14201269    .2373906  -.3274642
  1.728559 .0021184075 1.6106262  1.8693686   .8610587 1.4341735 1.4869235   .6570624   -.2997848 -.05148208  -.20466866  -.3681746
 1.1272026    -.372593  1.212596   .5961219    .180759 1.1527903  .9112204  1.1860987   -.4190056   .3606966    .3483753  -.4664214
 1.6196924  -1.0878006 1.5503187  1.5401485   .9652123  1.405004 1.3044976   .9574409   -.4065473 -.02455416    .1844317   -.373998
 1.5056424   -.3683792 1.3332113  1.4212747   .7770203 1.1954275 1.1437607    .785311   -.3092286  .25810617   .56740266 -.32323715
  1.282726     .205845  1.212596   .6463212   .3451319 1.0093964   1.08979   .4383182  -.08716769   2.742577    3.136969  .01147425
    .70729  -1.1298491 1.0075501   .3726539  .37262875  .8794734  .7818708   .7696331   -.3482224   .5229249    .9457151  -.3517827
-1.0034646   1.7755877 -.4036475  -.6278328 -1.8501546 -.8923425 -.4300005 -1.6966014   -.3109502  -.3610704     .391527 -.29748425
  1.246437   -.9759285 1.4719187  1.3754157  .52041066 1.2865056  1.336179   .6097085  -.49764425   1.257743   1.1656414  -.6408159
  1.500458    .3792053 1.3151193  1.3053873  .07811394 1.1298261 1.1770223   .5552128   -.1593708  .46543455    1.536108  -.1777862
  .8317088  -1.3447845 1.0135812   .3731301   .6929133  .9840326  .6790185   1.283678   -.3986953  1.1910012    .9722763 -.23162834
 1.2308847  -1.2608538  1.067858   .5192288   .9940391 1.0587906  .7394048   1.339018  -.46960175   .9131317      .82288  -.5141428
  1.500458  -.05199154 1.4236726    2.98555  .14874643 1.2074043 1.2558496  .58251035  -.09075636  -.4456538    .1587696 -.29061052
  .9198384   -.5452544 1.1824424   .7638975  -.6647866  .9156063 1.0937899  .19742955  -.10211507 -.27120057   -.2261627  -.1981203
 .09038173   .05829702  .5733356  .08742659   -.395222   .372394  .3658165   .3413597   -.3055966  -.4874498  -.59115374 -.13233459
 1.7130064  -1.1208919  1.592534   1.895689   .5839986 1.3767815 1.5103974   .4781129   -.2065549  -.3331513   -.4007307  -.2989657
 1.1531231   -.3323922 1.4900112  1.0228437   .1472184  1.262346  1.408353   .4094013   -.2100319 -.14977716    .1092432  -.2372051
 1.6456133   -1.148445  1.670934  2.3469555  1.2693384  1.526501 1.5009443   .8435919  -.48491335   -.778866   -.6675681 -.57370985
 1.1738596  -.25393164 1.0738887  .42798525   .4723604  .9894972  .8075164   .9993626   -.1930988   .3717652     .801386 -.05319968
 1.3708556      .37513 1.0377042    .672494   .7827792  .9178674  .8338687   .7538313   -.3058719  1.2702982   1.7467995  -.2220382
  .3081141    .4024772  .8386889   .2253189   .5650941  .7131135  .6250208   .6828517   -.4245126 -.17240983    .4712921 -.25075805
 1.0027843   -.4932146 1.0799193   .5307728  .04798828  .9407058  .8746041   .7229994  -.25138947  1.0119225   1.4265944  -.3537548
  1.593772    -.776904 1.3271806   .8162903   1.279053 1.2322757 1.1026405   .9637933  -.22210127   .1655932    .3313762 -.26530102
 1.4641695   -.4833039 1.3090882   .9878326    .309402 1.1345655  1.143564   .6360434  -.29045957   2.800068   2.9663236  -.3339077
  1.723375   -.7295067 1.5081033  1.7459277  1.0950915 1.3831347 1.3109626   .8917144   -.3461137  .02285873    .5611097  -.3952659
 1.6974543   -.7482518 1.6890262  2.6791315   .7458982  1.455103 1.6032313   .4798764  -.22941226 -.55386096   -.6774571  -.3560961
 1.1012821    .2719156 1.4477956  1.3422573 -.20272757  1.225045 1.3431492   .4495035    -.381348 .007825427    .3648024  -.5144617
  .6969219   -.5151795  1.544288  2.0933924  .46460176  1.422216 1.3923602   .8180048    -.331484  .03822257 -.016370486   -.453534
end