using adaptive Lasso regression select variables, but how to use the lasso results to predict income which is missing.

[roy zhong]

first,I use adaptive lasso select the variables:lasso linear income1 gender-popstu, selection(adaptive) stop(0) rseed(12345) nolog. and then obtain coefficient : lassocoef, display(coef) sort(coef) .
But how to predict income which is missing in the next step, many thanks!
clear
input double(income gender age age2 mar minzu hukou hukoulocal edu ind occu health urban region) byte(municipality prov_capital) double(grp pop studs_ratio miniwage grppop popgender popage popstu)
12000 1 .9036546096158996 .8827723918897483 1 1 1 1 4 3 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.8899324575790826 .09142445088910847
23000 1 .8193299386828627 .7774976570135173 1 1 1 0 3 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.8315930929204065 .09142445088910847
15000 1 1.325277964281084 1.4392245619498267 1 1 1 1 2 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 2.1816292808724636 .09142445088910847
8000 1 .7350052677498259 .6742281551825478 1 1 1 1 4 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.7732537282617304 .09142445088910847
30000 1 -1.8790595311743172 -1.5325308111277811 0 1 0 0 3 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 -.03526657615723008 .09142445088910847
20000 1 .2290572421516046 .09672103814722317 1 1 1 1 4 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.4232175403096736 .09142445088910847
40200 1 .8193299386828627 .7774976570135173 1 1 1 1 3 3 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.8315930929204065 .09142445088910847
60000 1 -.4455401253126904 -.5609954006985632 1 1 1 1 6 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .9565026230402643 .09142445088910847
35000 0 -1.288786834643059 -1.2026699751822572 1 1 1 0 6 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .37310897645350294 .09142445088910847
16000 0 -.023916770647506014 -.16496187425940828 1 1 1 1 4 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 1.248199446333645 .09142445088910847
15000 1 1.6625766480132314 1.9204804928125974 1 1 1 1 6 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 2.414986739507168 .09142445088910847
25000 0 -.023916770647506014 -.16496187425940828 1 1 1 1 4 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 1.248199446333645 .09142445088910847
122000 0 -.5298647962457272 -.6341864068506096 1 1 1 1 5 3 7 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .8981632583815882 .09142445088910847
75000 0 -.9514881509109117 -.9700629419319181 1 1 1 1 5 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .6064664350882075 .09142445088910847
25000 1 -1.6260855183752065 -1.4031932797084117 0 1 1 1 5 2 7 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .13975151781879835 .09142445088910847
90000 1 -1.2044621637100221 -1.1475260664375648 1 1 1 0 6 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .43144834111217906 .09142445088910847
45000 1 1.1566286224150102 1.2106279947900107 1 1 0 0 2 3 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 2.064950551555111 .09142445088910847
8000 0 -1.1201374927769854 -1.0903769246476107 1 1 1 0 4 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .48978770577085523 .09142445088910847
10000 1 1.1566286224150102 1.2106279947900107 1 1 1 1 3 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 2.064950551555111 .09142445088910847
10000 1 .8193299386828627 .7774976570135173 1 1 1 1 4 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.8315930929204065 .09142445088910847
3000 0 -.698514138111801 -.7745527200189176 1 1 1 1 4 3 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .7814845290642359 .09142445088910847
30000 1 .6506805968167889 .5729638863968398 1 1 1 1 4 2 2 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.7149143636030542 .09142445088910847
5000 1 1.4939273061471576 1.675842061290689 1 1 1 1 1 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 2.2983080101898157 .09142445088910847
30000 1 -1.373111505576096 -1.2558086508816881 1 1 1 1 6 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .31476961179482676 .09142445088910847
9000 0 .48203125495071525 .37645104796120854 1 1 1 1 3 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 1.598235634285702 .09142445088910847
16000 1 -1.7104101893082435 -1.4483110232267964 0 1 1 0 4 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .08141215316012221 .09142445088910847
60000 0 -1.4574361765091328 -1.3069420935358576 1 1 1 0 5 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .25643024713615065 .09142445088910847
65000 1 1.1566286224150102 1.2106279947900107 1 1 1 1 4 3 7 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 2.064950551555111 .09142445088910847
40000 1 -1.6260855183752065 -1.4031932797084117 0 1 1 1 5 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .13975151781879835 .09142445088910847
20000 0 -.3612154543796535 -.4857991615012553 1 1 1 0 3 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 1.0148419876989405 .09142445088910847
16800 1 .6506805968167889 .5729638863968398 1 1 1 1 4 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.7149143636030542 .09142445088910847
13000 0 -1.7104101893082435 -1.4483110232267964 1 1 1 0 6 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .08141215316012221 .09142445088910847
40000 1 -1.5417608474421698 -1.3560703031447654 0 1 1 0 6 0 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .1980908824774745 .09142445088910847
24000 0 -1.4574361765091328 -1.3069420935358576 1 0 1 1 5 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .25643024713615065 .09142445088910847
6000 1 -1.373111505576096 -1.2558086508816881 0 1 1 1 6 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .31476961179482676 .09142445088910847
850 1 .8193299386828627 .7774976570135173 1 1 1 1 3 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.8315930929204065 .09142445088910847
26000 1 .2290572421516046 .09672103814722317 1 1 1 1 5 2 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.4232175403096736 .09142445088910847
12000 1 -1.1201374927769854 -1.0903769246476107 0 1 1 1 4 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .48978770577085523 .09142445088910847
24000 0 .6506805968167889 .5729638863968398 1 1 1 1 4 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 1.7149143636030542 .09142445088910847
13000 0 1.2409532933480472 1.323923661847288 1 1 0 1 4 3 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 2.1232899162137873 .09142445088910847
30000 1 -1.288786834643059 -1.2026699751822572 1 1 1 1 5 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .37310897645350294 .09142445088910847
10000 1 1.7469013189462683 2.0458075581414437 1 0 1 1 4 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 2.473326104165844 .09142445088910847
10000 0 -.4455401253126904 -.5609954006985632 1 1 0 1 4 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .9565026230402643 .09142445088910847
20000 0 -.9514881509109117 -.9700629419319181 1 1 1 0 4 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .6064664350882075 .09142445088910847
12000 1 .48203125495071525 .37645104796120854 1 1 1 1 4 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.598235634285702 .09142445088910847
100000 1 .9879792805489365 .990052359811241 1 1 0 0 5 3 2 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.9482718222377589 .09142445088910847
43200 1 -1.0358128218439484 -1.0312225498123953 1 1 1 0 5 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .5481270704295313 .09142445088910847
20000 1 -.8671634799778748 -.9068981010061795 1 1 1 1 4 0 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .6648057997468837 .09142445088910847
25000 0 -1.373111505576096 -1.2558086508816881 1 1 1 1 4 3 7 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .31476961179482676 .09142445088910847
80000 1 -.19256611251357977 -.32939098397085487 1 0 1 0 6 3 7 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.1315207170162929 .09142445088910847
10000 1 -1.963384202107354 -1.5716328555103813 0 1 0 0 3 3 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 -.09360594081590623 .09142445088910847
12000 1 -.10824144158054289 -.24817904563776236 1 1 0 0 3 3 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.189860081674969 .09142445088910847
13000 0 .6506805968167889 .5729638863968398 1 0 1 0 4 3 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 1.7149143636030542 .09142445088910847
6000 1 -.2768907834466166 -.40859768925868584 1 1 0 0 4 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.0731813523576166 .09142445088910847
11000 0 .6506805968167889 .5729638863968398 1 1 1 1 4 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 1.7149143636030542 .09142445088910847
40000 1 -1.2044621637100221 -1.1475260664375648 0 1 1 1 6 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .43144834111217906 .09142445088910847
30000 1 -.5298647962457272 -.6341864068506096 1 1 1 1 4 3 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .8981632583815882 .09142445088910847
43200 1 -1.0358128218439484 -1.0312225498123953 0 1 1 1 6 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .5481270704295313 .09142445088910847
23000 0 -1.5417608474421698 -1.3560703031447654 0 1 1 0 4 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .1980908824774745 .09142445088910847
24000 0 -1.1201374927769854 -1.0903769246476107 1 1 0 0 3 3 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .48978770577085523 .09142445088910847
10000 1 .7350052677498259 .6742281551825478 1 1 1 1 4 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.7732537282617304 .09142445088910847
35000 0 -1.0358128218439484 -1.0312225498123953 0 1 1 1 5 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .5481270704295313 .09142445088910847
30000 1 -.10824144158054289 -.24817904563776236 1 1 1 1 4 3 2 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.189860081674969 .09142445088910847
23000 1 .3133819130846415 .18795914170662342 1 1 1 1 3 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.4815569049683497 .09142445088910847
100000 1 -1.373111505576096 -1.2558086508816881 1 1 0 0 3 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .31476961179482676 .09142445088910847
25000 1 .2290572421516046 .09672103814722317 1 1 1 0 4 3 2 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.4232175403096736 .09142445088910847
15000 0 -.4455401253126904 -.5609954006985632 1 1 0 0 4 2 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .9565026230402643 .09142445088910847
30000 1 -1.0358128218439484 -1.0312225498123953 1 1 1 0 6 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .5481270704295313 .09142445088910847
14000 0 -.19256611251357977 -.32939098397085487 1 1 1 1 5 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 1.1315207170162929 .09142445088910847
36000 0 1.4096026352141209 1.556530695097627 0 1 1 0 5 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 2.2399686455311394 .09142445088910847
14000 0 .6506805968167889 .5729638863968398 0 1 1 1 5 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 1.7149143636030542 .09142445088910847
30000 1 -1.4574361765091328 -1.3069420935358576 1 1 1 0 7 3 6 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .25643024713615065 .09142445088910847
40000 1 -.10824144158054289 -.24817904563776236 1 1 0 0 4 3 7 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.189860081674969 .09142445088910847
36000 1 -1.6260855183752065 -1.4031932797084117 0 1 1 1 4 3 5 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .13975151781879835 .09142445088910847
140000 1 -.2768907834466166 -.40859768925868584 1 1 1 0 4 3 7 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.0731813523576166 .09142445088910847
20000 1 .6506805968167889 .5729638863968398 1 1 1 1 5 0 0 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 1.7149143636030542 .09142445088910847
12000 1 -.8671634799778748 -.9068981010061795 1 1 0 0 3 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 2.0391475732741338 .6648057997468837 .09142445088910847
12000 0 -.5298647962457272 -.6341864068506096 1 1 1 1 4 3 4 3 1 3 1 0 2.114580593626381 1.427526295729116 -.492691300783646 .8903201011726182 1.864472886803907 -.75907363723971 .8981632583815882 .09142445088910847
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end

[roy zhong]

help

[Justin Niakamal]

After running your model:

predict income_hat

See help lasso postestimation. Also see
https://www.stata.com/meeting/chicag...cago19_Liu.pdf

[roy zhong]

After running your model:

predict income_hat

See help lasso postestimation. Also see
https://www.stata.com/meeting/chicag...cago19_Liu.pdf

thanks very much！！！

[roy zhong]

After running your model:

predict income_hat

See help lasso postestimation. Also see
https://www.stata.com/meeting/chicag...cago19_Liu.pdf

I run it ,however it shows that "predict income_hat
last estimates not found
r(301);"
could you kindly guide me？

[Justin Niakamal]

Try

lasso linear income gender-popstu, selection(adaptive) stop(0) rseed(12345) nolog
predict income_hat
lassocoef, display(coef) sort(coef)

You should see

predict income_hat
(options xb penalized assumed; linear prediction with penalized coefficients)

[roy zhong]

Try

lasso linear income gender-popstu, selection(adaptive) stop(0) rseed(12345) nolog
predict income_hat
lassocoef, display(coef) sort(coef)

You should see

predict income_hat
(options xb penalized assumed; linear prediction with penalized coefficients)

thanks, but it's not the answer. As it shows that income variable is zero from 81 to 99，I want to use the information of 1 to 80，including income，to predict the lack of income from 81-99.

[Justin Niakamal]

replace income = . if income == 0
lasso linear income gender-popstu if !missing(income), selection(adaptive) stop(0) rseed(12345) nolog
predict income_hat

[roy zhong]

replace income = . if income == 0
lasso linear income gender-popstu if !missing(income), selection(adaptive) stop(0) rseed(12345) nolog
predict income_hat

thank for your kind help, it works!!!