mlogit for panel data/ cross-sectional data

[Itesh New]

Dear all, I am working on inflation convergence using PS methodology and to account for reason behind formation of different clubs, I have thought of using mlogit model. However, when I use xtlogit, it suggests that outcome does not vary. Is there any way to go around since PS methodology gives clubs that are non-varying with years/time, it gives club considering panel of data. Can I use mlogit and run it for panel of data or should I take average of the relevant variables and then form a cross-sectional data and then run mlogit command?

xtset code1 date
xtlogit club_group IFI_index1 rel_manu_gdp
outcome does not vary; remember:
0 = negative outcome,
all other nonmissing values = positive outcome

* Example generated by -dataex-. To install: ssc install dataex
clear
input float date long code1 float(club_group rel_manu_gdp rel_agri_gdp rel_industry_gdp rel_fis_def IFI_index1 ln_wage dev_temp)
2011  2 4  .13364772    .13716458  .2969404    .04059546  .21019286         . -.0881646
2012  2 4  .10558365      .138112 .25585863    .04599755   .4216687         .  .1682665
2013  2 4  .09475113    .14891067 .24402127    .04431429   .7265298         . -.1028404
2014  2 4   .1098343    .13717477 .25157568     .0714115   .8131914         .  .1097339
2015  2 4   .1102452    .11724608 .24840277    .04384823  .08404899         .  .1376355
2016  2 4   .1120763    .11903419  .2578909    .05721645  .15691006         .  .3191977
2017  2 4  .11047008    .12462392  .2477202    .05443251   .2097996 13.027692  .3681092
2018  2 4  .11412922    .11059942 .24257964    .05655952   .2446741 15.512598  .1165363
2019  2 4  .11017014    .12013259   .232954    .06107013    .293414   12.9836  .4634914
2020  2 4  .11987834    .11887106 .24817663     .0870526   .3152951 13.043105  .1767556
2021  2 4  .11576699     .1119776 .24674565    .05422694   .3907037 15.528012  .1357741
2022  2 4  .11339237    .11045721  .2436204    .06459172  .39962345 18.012918  .1500955
2023  2 4          .            .         .            .          .         .         .
2011  3 5 .011660817    .23124123  .1894485     .0892188          0         .  .1265991
2012  3 5 .012422948     .2487351  .1922765   .020532336  .08393866         .  -.114011
2013  3 5 .014818363     .2396782  .1983983    .13015199   .1901216         . -.0031983
2014  3 5  .04517251     .2111921 .25719827   -.03608517   .3385143         .  .2783772
2015  3 5  .03108818     .1879848 .23806113  -.013342266   .4592521         . -.0175695
2016  3 5  .03309214    .12803085   .258107   -.05673936   .5457181         .   .279194
2017  3 5 .023690905    .12491732  .2582766   .020099895   .7220258 13.241215  .3618815
2018  3 5  .02905441    .11710556 .23887804    .11854781   .7410245  15.72612  .0198359
2019  3 5 .010959593    .10124183  .2076325    .05391762   .7925097 13.313251  .2176776
2020  3 5  .01078783    .11145417 .18488836    .05885748   .9052114 13.335074  .1146215
2021  3 5 .009683074     .1132707 .20776464     .0381808    .957505 15.819982  .6004884
2022  3 5          .            .         .            .          . 18.304888  .1509927
2023  3 5          .            .         .            .          .         .         .
2011  4 1  .10757855    .14398381  .3063298   .011496427          0         .  .1819623
2012  4 1   .0968183     .1694628 .28212556    .01028896  .09451348         . -.2963088
2013  4 1  .10831022     .1535468 .29068735    .02447494  .23327303         . -.1928473
2014  4 1  .10912807    .14785127 .29038897   .032866802   .3424987         .  .1662195
2015  4 1  .13248754    .13392639  .3566148   -.01572401   .4330762         .   -.10321
2016  4 1  .14572698    .12993453  .3687206     .0303146   .5156338         .  .2438151
2017  4 1  .15203854    .12109897  .3800024    .04225185   .6239421 12.937557  .2194942
2018  4 1   .1455639    .11769339  .3760103    .02068477   .6786715 15.422463 -.1349764
2019  4 1   .1441306    .10758002 .36418685    .06155195   .7081159  12.93864  .1268125
2020  4 1   .1710941    .11468346  .3546855    .04572213   .8940912 12.987007 -.0214138
2021  4 1   .1719662    .11436927  .3547587    .14080316   .9556162 15.471913  .4122375
2022  4 1  .17062534    .12029167  .3519474    .05309278   .9835006  17.95682  .1435409
2023  4 1          .            .         .            .          .         .         .
2011  5 3  .05934072    .17240065 .18426508    .02393342          0         .  .0727502
2012  5 3  .03781968    .18490377   .154001     .0254817  .08815807         . -.0730349
2013  5 3  .07006434    .13761231    .19161   .030973503   .2115996         . -.2534569
2014  5 3  .09287879    .12613977 .20332025    .03999536  .39778405         . -.2194331
2015  5 3  .07887074    .11916047  .1960075     .0406829    .516402         . -.2582089
2016  5 3  .09151776    .12164711   .202396    .05169113   .6195814         .  .2546462
2017  5 3  .08891127    .12125135  .1965994    .04158094   .6984116 12.907267  .0131157
2018  5 3  .07913925    .10295621 .19219394   .036202498   .6965202 15.392173 -.3594004
2019  5 3  .07792466    .09299076 .19387235    .03073086   .7850489 12.943005 -.1840746
2020  5 3  .09584175    .10828561 .20592533    .08089057   .9026229 13.025047 -.4367913
2021  5 3  .09333328    .10264447   .205436    .19212873   .9425674 15.509953 -.0975696
2022  5 3  .08530129     .0973092 .19845265     .0585008   .9887738  17.99486 -.1054478
2023  5 3          .            .         .            .          .         .         .
2011  7 3  .15404397    .11376065   .443882     .0050546          0         . -.0727374
2012  7 3  .16027357     .1159035  .4414122   .016008204  .09993458         .  .0652151
2013  7 3   .1965676    .10859328  .4638433   .027697517  .24734175         . -.3087138
2014  7 3   .1783202    .11010426  .4486982    .04345757   .3842968         . -.1213748
2015  7 3   .1785314     .1038365  .4459601   .028564867   .5421306         .  .0243381
2016  7 3  .14826572    .11483993  .4183447    .01893734   .5821364         .    .31568
2017  7 3   .1591746    .09060244  .4439256   .030935464   .6772299 12.716412  .2611257
2018  7 3   .1822016     .0932191  .4646529   .033907223   .6812004  15.20132 -.0655535
2019  7 3  .18032564    .09243255  .4624621    .07150114   .7722453 12.794374  .1447752
2020  7 3   .1825388    .09748602   .473023    .06410766   .8858902 12.820193 -.1791952
2021  7 3  .18108575    .09462006   .471306    .05699698   .9230224   15.3051 -.0074331
2022  7 3  .17609265    .09333882   .470567    .05050463          1 17.790007  .1225955
2023  7 3          .            .         .            .          .         .         .
2011 10 2   .0549954  .0019187167 .13790348   .007402613 .018350184         .  .0657433
2012 10 2  .06010157  .0016611917 .14167967    .00623247  .17297143         . -.0997046
2013 10 2  .05838257  .0014045768 .14338887   .010035418  .36036325         . -.1154219
2014 10 2  .04808797  .0006478736   .127157  -.000511258   .4927274         . -.2030652
2015 10 2  .05898893  .0004540996  .1366208   -.00280054  .54021645         .  .0907878
2016 10 2   .0525074  .0004255076 .13101213   .002053676   .5916141         .   1.03549
2017 10 2  .04980943    .00036713 .13400243 -.0002084813   .6058464 13.448195  .6469594
2018 10 2   .0487739  .0003601456 .13658932 -.0039570024   .6049827 15.933102  .6097534
2019 10 2  .04937278  .0003656458 .13338073  .0007113997   .7175361 13.540215 -.1080624
2020 10 2  .04732624  .0003719676 .13707814    .01224798   .7294692  13.49775 -.1103298
2021 10 2  .04814519 .00035945585 .14245014    .02436076   .6370811 15.982656  .3023385
2022 10 2  .04473269  .0002915041 .13676794   .014087196   .5716644 18.467564  .5862077
2023 10 2          .            .         .            .          .         .         .
2011 11 1   .3845821    .02417396  .5911835    .02077105          0         . -.0611753
2012 11 1   .3875519   .028705264  .5124155   .031994224     .20921         .  .1367269
2013 11 1   .3219001   .034763813  .4138453    .04292259   .4920111         . -.0459024
2014 11 1   .4280626    .02664341  .5096528   .023681035     .63551         .  .2132977
2015 11 1   .4320555    .02234738 .52353245   .032153882   .6919079         .  .3328916
2016 11 1   .4204743    .02158139  .5492415   .018244173   .7019309         .  .2420738
2017 11 1  .41969025   .020826096 .54403186   .030577734   .7585647 13.475726   .388555
2018 11 1   .4318907   .021023497 .53929186   .033978473   .7956978 15.960633  .2575773
2019 11 1    .412639    .01825528 .52508324     .0344453   .8381343 13.408748  .3871169
2020 11 1   .4167816   .018844806  .5361619    .05116186   .8966638 13.443605  .2795146
2021 11 1   .3898055   .018161524  .5116384    .12115724   .9119023 15.928513  .1449199
2022 11 1          .            .         .            .          . 18.413418  .1391195
2023 11 1          .            .         .            .          .         .         .
2011 12 1   .2547397    .12915681  .3929843   .017912429          0         . -.0449343
2012 12 1   .2832849    .09231366 .41995105   .024158785   .1305427         . -.2809364
2013 12 1  .27017832    .11780458  .3991748    .02508975   .3360414         . -.2638389
2014 12 1   .2960798    .10378323 .41830295    .02257626  .53821313         . -.0035613
2015 12 1    .311031    .08708879  .4417341   .025730455    .669616         .  .3410608
2016 12 1   .3141135    .08528277  .4366754   .016793331   .7199525         .  .3478894
2017 12 1   .3157508    .08618946   .434054    .01966372   .7750405 12.963132  .2837966
2018 12 1   .3268391    .06641738 .44291425    .02228619   .7868733  15.44804  .4761974
2019 12 1   .3231928    .07153846  .4356183    .01942815   .8652374  12.92704  .1355885
end

[Richard Williams]

There is an xtmlogit command. Have you tried it?

[Itesh New]

Richard Williams Dear Prof. Williams I am using STATA 16, and it seems like it doesn't have the capability to run that particular command. I tries running it and it says command is unrecognized. However, when I tried running the command, for example "mlogit club_group IFI_index1 rel_manu_gdp rel_agri_gdp rel_industry_gdp rel_construction_gdp rel_services_gdp rel_fis_def ln_wage" for the above dataset which is a panel data, it gives results but I am worried how it has considered the time dimensions while estimating the coefficients of the results and the results may be wrong.

[Itesh New]

Richard Williams When I tried running xtmlogit, i.e. "xtmlogit club_group IFI_index1 rel_manu_gdp" it says that the outcome does not vary in group.