Below is an example of my data set. I want to perform the Kernel-based Propensity Score Matching diff-in-diff. I am actually using the following command. However, it is giving me an "error 2000". Please, would someone help in letting me know where are the mistakes in the command and how to run the regression?
Code:
#delimit; diff mw, treat_interaction cov(county unemployment population median_age median_income Graduates ghi) kernel ktype (gaussian) id(county) support bw (0.05) cluster (county) bs reps(50) robust ;
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input str17 county double unemployment long population double median_age long median_income double Graduates float(ghi treat_interaction) "alameda" 9.2 1494876 36.4 70821 16.6 247.483 1 "butte" 13.1 221578 36.9 43165 8.4 120.275 1 "calaveras" 11.8 45507 49.5 54686 6 225.646 0 "contracosta" 9.5 1037817 38.3 79135 13.9 247.483 1 "humboldt" 10.5 135034 37.4 42197 9.5 156.278 0 "inyo" 7.1 18457 45.1 49571 8.1 223.37 0 "lassen" 9.4 31945 36.4 51457 4.9 198.262 0 "losangeles" 11 9974203 35.3 55870 10.4 236.282 1 "marin" 7.6 254643 44.8 90839 23.4 162.841 1 "merced" 15.7 265001 30.6 44397 4.8 228.114 1 "nevada" 10.7 98606 48.8 56949 11.4 120.275 0 "placer" 10 355924 40.4 72725 11.4 211.771 1 "plumas" 17.2 19586 49.9 45794 9.4 211.771 0 "riverside" 11.2 2109464 33.4 57768 7.2 221.971 1 "sanbenito" 13.8 56115 34.5 66237 4.7 232.761 0 "sanbernardino" 14.5 2056915 31.9 54090 6.5 280.19 1 "sanluisobispo" 5.5 278680 39 64014 12.4 306.788 1 "santaclara" 7.8 1739396 35.8 86850 19.6 341.417 1 "santacruz" 7.6 269278 37 67256 15 216.777 1 "sierra" 9.1 3163 51.6 42500 5.6 198.943 0 "solano" 11.4 425753 37.3 66828 7.4 216.777 1 "sonoma" 8.7 478551 39.7 64343 11.3 247.483 1 "stanislaus" 16.3 522794 33.3 49573 5.4 122.71 0 "ventura" 8.6 840833 37.1 77348 11.7 187.689 1 "yuba" 12.7 73897 32.2 48739 4.9 198.262 1 "alameda" 7.1 1605217 37.2 79831 18.5 215.606 1 "alameda" 7.7 1457095 36.1 672 16 307.61 0 "alameda" 8.5 1477980 36.2 69384 16.3 341.417 1 "alameda" 9.6 1559308 36.9 73775 17.5 213.38 1 "alameda" 9.9 1515136 36.6 71516 16.8 240.482 1 "alameda" 8.3 1584983 37.1 75619 18 216.777 1 "alameda" 10.3 1535248 36.8 72112 17.2 162.841 1 "amador" 11.4 38327 47.2 54758 5.8 217.474 0 "amador" 17.3 37764 48.4 53462 4.8 225.646 0 "amador" 15.2 38244 48 56180 5.7 223.37 0 "amador" 16.8 37422 49.1 53684 5.1 232.761 0 "amador" 14 36995 50 54171 6.6 91.5255 0 "amador" 10.4 38039 46.3 3857 6.1 206.85 0 "amador" 15.9 37159 49.6 52964 6.2 122.71 0 "amador" 11.6 36963 50.3 57032 6.9 228.114 0 "butte" 14.4 220101 37.3 43339 8.1 198.943 0 "butte" 13.2 219309 37.2 42971 7.9 194.679 0 "butte" 10.6 217917 36.6 1295 8.4 296.228 0 "butte" 10.7 223877 36.9 44366 8.7 198.262 1 "butte" 14.1 220542 37 43752 8.2 211.771 0 "butte" 11.5 218635 37.2 43170 8.1 189.696 0 "butte" 12.1 222564 36.8 43444 8.3 156.278 1 "calaveras" 9.6 44787 51.2 53502 6.5 228.114 0 "calaveras" 12.1 44767 50.7 53233 6.5 91.5255 0 "calaveras" 9.2 45794 49 55256 6.4 223.37 0 "calaveras" 8.2 46548 47.7 3490 6.8 206.85 0 "calaveras" 7.6 45994 48.5 54971 6.6 217.474 0 "calaveras" 11.6 45147 50 55295 6.2 232.761 0 "calaveras" 11.3 44921 50.3 54936 6.1 122.71 0 "colusa" 13.8 21297 33.5 49558 2.9 194.679 0 "colusa" 12.9 21001 33 4129 3.7 296.228 0 "colusa" 10.4 21396 34 52168 3.2 156.278 1 "colusa" 12.4 21424 34.1 50503 3.1 120.275 0 "colusa" 13.3 21366 33.9 52158 2.5 211.771 0 "colusa" 14.4 21329 33.9 52165 2.5 198.943 0 "colusa" 8.7 21361 34.7 54946 3.4 198.262 1 "colusa" 14.2 21165 34 48016 2.9 189.696 0 "contracosta" 7.2 1015571 38 718 13.4 307.61 0 "contracosta" 8.2 1024809 38.1 78385 13.7 341.417 0 "contracosta" 7.7 1107925 39.1 82881 14.7 215.606 1 "contracosta" 10.4 1065794 38.6 78756 14.1 162.841 1 "contracosta" 8.8 1096068 38.8 80185 14.4 216.777 1 "contracosta" 10.1 1052047 38.5 78187 14.1 240.482 1 "contracosta" 9.8 1081232 38.7 79799 14.3 213.38 1 "eldorado" 9.6 183000 45.2 72586 12 198.262 1 "eldorado" 8.2 179053 42.6 70000 10.1 189.696 0 "eldorado" 7 175941 41.6 1640 10 296.228 0 "eldorado" 11.1 182093 44.9 69584 11.3 156.278 0 "eldorado" 12 180982 44.1 69297 10.4 211.771 0 "eldorado" 11.3 181465 44.4 68507 10.8 120.275 0 "eldorado" 10.9 180441 43.5 70117 10 198.943 0 "eldorado" 9.7 179878 43.1 68815 9.7 194.679 0 "fresno" 14.3 948844 31.2 45201 6.4 122.71 1 "fresno" 14.5 939605 30.9 45563 6.5 232.761 1 "fresno" 12.7 920623 30.6 46903 6.3 223.37 0 "fresno" 11.4 908830 30.4 46430 6.3 217.474 0 "fresno" 10 890750 30.3 597 6.1 206.85 0 "fresno" 14 930517 30.7 45741 6.3 225.646 1 "fresno" 11.9 963160 31.6 45963 6.6 228.114 1 "fresno" 13.2 956749 31.4 45233 6.4 91.5255 1 "glenn" 7.1 27891 34.5 1743 3.3 296.228 0 "glenn" 9.9 27976 36.9 41699 3.2 198.262 1 "glenn" 13.5 28019 36.7 40106 5 120.275 0 "glenn" 12.6 28029 37.2 39349 4.2 156.278 1 "glenn" 12.8 28054 37.2 43023 4.5 211.771 0 "glenn" 10 28027 35.3 43239 4.3 194.679 0 "glenn" 10.5 28078 36.5 42641 4.4 198.943 0 "glenn" 8.3 27935 35.1 43074 4.3 189.696 0 "humboldt" 7.9 129003 36.5 1423 9 296.228 0 "humboldt" 11.1 134613 37.5 41426 8.8 211.771 0 "humboldt" 11.3 134876 37.4 42153 9 120.275 0 "humboldt" 8.6 133058 37.1 40089 8.6 189.696 0 "humboldt" 10.4 134317 37.5 40830 8.8 198.943 0 "humboldt" 9.5 135182 37.6 42685 9.5 198.262 1 "humboldt" 9.7 133585 37.3 40376 8.6 194.679 0 end
Ali
0 Response to Kernel-based Propensity Score Matching diff-in-diff
Post a Comment