insongkim / PanelMatch

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Troubles with matched.set #55

Closed einter-dot closed 4 years ago

einter-dot commented 4 years ago

Hi, I am new of PanelMatch command. I tried several times to run it but I always got the following error: "Error in perform_refinement(lag, time.id, unit.id, treatment, refinement.method, : no viable treated units for ate specification"

Alternatively, when quoi=att, the outcomes for each treated units is always the same (all units matched with the entire matched.set - and eventually with the same weight- no matter the refinement.method applied).

here below the print(meets):

1 41 1999 965 2 48 1999 965 3 50 1999 965 4 52 1999 965 5 53 1999 965 6 59 1999 965 7 60 1999 965 8 68 1999 965 9 70 1999 965 10 80 1999 965 11 87 1999 965 12 89 1999 965 13 91 1999 965 14 92 1999 965 15 98 1999 965 16 99 1999 965 17 107 1999 965 18 109 1999 965 19 353 1999 965 20 354 1999 965 21 362 1999 965 22 364 1999 965 23 365 1999 965 24 371 1999 965 25 372 1999 965 26 380 1999 965 27 382 1999 965 28 431 1999 965 29 432 1999 965 30 439 1999 965 31 442 1999 965 32 443 1999 965 33 449 1999 965 34 450 1999 965 35 458 1999 965 36 460 1999 965 37 509 1999 965 38 510 1999 965 39 517 1999 965 40 519 1999 965 41 521 1999 965 42 527 1999 965 43 528 1999 965 44 536 1999 965 45 538 1999 965 46 548 1999 965 47 549 1999 965 48 556 1999 965 49 558 1999 965 50 560 1999 965 51 566 1999 965 52 567 1999 965 53 575 1999 965 54 577 1999 965 55 782 1999 965 56 783 1999 965 57 790 1999 965 58 792 1999 965 59 794 1999 965 60 795 1999 965 61 801 1999 965 62 809 1999 965 63 811 1999 965 64 821 1999 965 65 822 1999 965 66 829 1999 965 67 831 1999 965 68 833 1999 965 69 834 1999 965 70 840 1999 965 71 848 1999 965 72 850 1999 965 73 1133 1999 965 74 1134 1999 965 75 1141 1999 965 76 1143 1999 965 77 1145 1999 965 78 1146 1999 965 79 1152 1999 965 80 1153 1999 965 81 1162 1999 965 82 1211 1999 965 83 1212 1999 965 84 1219 1999 965 85 1221 1999 965 86 1223 1999 965 87 1224 1999 965 88 1230 1999 965 89 1231 1999 965 90 1239 1999 965

Any suggestion or hint on how to sort it out is more than welcome! Many thanks

adamrauh commented 4 years ago

Could you create and share a plot using the DisplayTreatment function? I would be curious to see the structure of your data. The first error you describe suggests that the data does not allow for an ATC to be calculated. I suspect you have a rather distinct distribution of treatment in your data. But, it's hard to say what is happening without knowing more.

einter-dot commented 4 years ago

First of all, thanks a lot for your prompt reply! Here below the plot of the treatment distribution: image Because of your comment, I also tried with a slightly different dataset where the treatment is allowed to be time variant for some units. However, the outcome for the matched.set is similar (all units match with the entire matched.set). Here below you'll find the plot of the treatment distribution for this second case: image Thanks a lot for your help.

adamrauh commented 4 years ago

Happy to be of help @einter-dot . Given the treatment distribution, it makes sense that an ATC is not able to be calculated. The ATC calculation essentially works by "flipping" the treatment variable (changing 0 to 1 and 1 to 0). So imagine the red and blue parts of the plot being flipped and it should make sense.

Have you looked at the weights associated with the units within a matched.set? This guide might help: https://github.com/insongkim/PanelMatch/wiki/Matched-Set-Objects

The summary view will tell you how many control units were matched based on treatment history, but you can check refinement by looking at the weights within each set, or using the get_covariate_balance function for a broader view. If changing the refinement method isn't doing anything to change the weights, then something is most likely going wrong inside the code!

einter-dot commented 4 years ago

Many thanks. I fully agree with you about the ATC. Regarding the weights, I carried out all the checks and unfortunately I can confirm that changing the refinement method does not change the weights (that are always the same for all treated units). Here below an example of two treated units. What do you mean with "going wrong inside the code"? Can we fix it?

$1231.1999 [1] "1" "2" "3" "4" "5" "6" "8" "9" "10" "11" "13" "14" "15" "17" "18" "19" "20" "21" "22" "23" "24" "26" "27" "29" "30" "31" "32" "33"
[29] "36" "37" "38" "39" "40" "42" "43" "44" "45" "47" "49" "54" "56" "57" "58" "61" "62" "63" "65" "66" "69" "71" "72" "75" "76" "77" "78" "79"
[57] "81" "82" "83" "84" "86" "88" "93" "95" "96" "97" "100" "101" "102" "104" "105" "108" "110" "111" "114" "115" "116" "117" "118" "119" "120" "121" "122" "123" [85] "125" "126" "127" "128" "130" "131" "132" "134" "135" "136" "137" "138" "139" "140" "141" "143" "144" "146" "147" "148" "149" "150" "153" "154" "155" "156" "157" "158" [113] "159" "160" "161" "162" "164" "165" "166" "167" "169" "170" "171" "173" "174" "175" "176" "177" "178" "179" "180" "182" "183" "185" "186" "187" "188" "189" "192" "193" [141] "194" "195" "196" "197" "198" "199" "200" "201" "203" "204" "205" "206" "208" "209" "210" "212" "213" "214" "215" "216" "217" "218" "219" "221" "222" "224" "225" "226" [169] "227" "228" "231" "232" "233" "234" "235" "236" "237" "238" "239" "240" "242" "243" "244" "245" "247" "248" "249" "251" "252" "253" "254" "255" "256" "257" "258" "260" [197] "261" "263" "264" "265" "266" "267" "270" "271" "272" "273" "313" "314" "315" "316" "317" "318" "319" "321" "322" "323" "325" "326" "327" "329" "330" "331" "332" "333" [225] "334" "335" "336" "338" "339" "341" "342" "343" "344" "345" "348" "349" "350" "351" "352" "355" "356" "357" "358" "360" "361" "366" "368" "369" "370" "373" "374" "375" [253] "377" "378" "381" "383" "384" "387" "388" "389" "390" "391" "392" "393" "394" "395" "396" "397" "399" "400" "401" "403" "404" "405" "407" "408" "409" "410" "411" "412" [281] "413" "414" "416" "417" "419" "420" "421" "422" "423" "426" "427" "428" "429" "430" "433" "434" "435" "436" "438" "440" "444" "446" "447" "448" "451" "452" "453" "455" [309] "456" "459" "461" "462" "465" "466" "467" "468" "508" "511" "512" "513" "514" "516" "518" "522" "524" "525" "526" "529" "530" "531" "533" "534" "537" "539" "540" "543" [337] "544" "545" "546" "547" "550" "551" "552" "553" "555" "557" "561" "563" "564" "565" "568" "569" "570" "572" "573" "576" "578" "579" "582" "583" "584" "585" "586" "587" [365] "588" "589" "590" "591" "592" "594" "595" "596" "597" "599" "600" "602" "603" "604" "605" "606" "607" "608" "609" "611" "612" "614" "615" "616" "617" "618" "621" "622" [393] "623" "624" "664" "665" "666" "667" "668" "669" "670" "672" "673" "674" "675" "677" "678" "679" "681" "682" "683" "684" "685" "686" "687" "689" "690" "692" "693" "694" [421] "695" "696" "699" "700" "701" "702" "703" "704" "705" "706" "707" "708" "709" "711" "712" "713" "714" "716" "717" "718" "720" "721" "722" "723" "724" "725" "726" "728" [449] "729" "731" "732" "733" "734" "735" "738" "739" "740" "741" "742" "743" "744" "745" "746" "747" "748" "750" "751" "752" "753" "755" "756" "757" "759" "760" "761" "762" [477] "763" "764" "765" "767" "768" "770" "771" "772" "773" "774" "777" "778" "779" "780" "781" "784" "785" "786" "787" "789" "791" "796" "798" "799" "800" "802" "803" "804" [505] "806" "807" "810" "812" "813" "816" "817" "818" "819" "820" "823" "824" "825" "826" "828" "830" "835" "837" "838" "839" "841" "842" "843" "845" "846" "849" "851" "852" [533] "855" "856" "857" "858" "859" "860" "861" "862" "863" "864" "865" "867" "868" "869" "870" "872" "873" "874" "876" "877" "878" "879" "880" "881" "882" "884" "885" "887" [561] "888" "889" "890" "891" "894" "895" "896" "897" "898" "899" "900" "901" "902" "903" "904" "906" "907" "908" "909" "911" "912" "913" "915" "916" "917" "918" "919" "920" [589] "921" "923" "924" "926" "927" "928" "929" "930" "933" "934" "935" "936" "937" "938" "939" "940" "941" "942" "943" "945" "946" "947" "948" "950" "951" "952" "954" "955" [617] "956" "957" "958" "959" "960" "962" "963" "965" "966" "967" "968" "969" "972" "973" "974" "975" "1015" "1016" "1017" "1018" "1019" "1020" "1021" "1023" "1024" "1025" "1026" "1028" [645] "1029" "1030" "1032" "1033" "1034" "1035" "1036" "1037" "1038" "1039" "1043" "1044" "1045" "1046" "1047" "1050" "1051" "1052" "1053" "1054" "1055" "1056" "1057" "1058" "1059" "1060" "1062" "1063" [673] "1064" "1065" "1067" "1068" "1069" "1071" "1072" "1073" "1074" "1075" "1076" "1077" "1078" "1080" "1082" "1083" "1084" "1085" "1086" "1089" "1090" "1091" "1092" "1132" "1135" "1136" "1137" "1138" [701] "1140" "1142" "1147" "1149" "1150" "1151" "1154" "1155" "1156" "1158" "1159" "1161" "1163" "1164" "1167" "1168" "1169" "1170" "1171" "1172" "1173" "1174" "1175" "1176" "1177" "1179" "1180" "1181" [729] "1182" "1184" "1185" "1186" "1188" "1189" "1190" "1191" "1192" "1193" "1194" "1195" "1197" "1198" "1200" "1201" "1202" "1203" "1206" "1207" "1208" "1209" "1210" "1213" "1214" "1215" "1216" "1218" [757] "1220" "1225" "1227" "1228" "1229" "1232" "1233" "1234" "1236" "1237" "1240" "1241" "1242" "1245" "1246" "1247" "1248" "1249" "1250" "1251" "1252" "1253" "1254" "1255" "1257" "1258" "1259" "1260" [785] "1262" "1263" "1264" "1266" "1267" "1268" "1269" "1270" "1271" "1272" "1273" "1275" "1276" "1278" "1279" "1280" "1281" "1284" "1285" "1286" "1287" "1288" "1289" "1290" "1291" "1292" "1293" "1294" [813] "1296" "1297" "1298" "1299" "1301" "1302" "1303" "1305" "1306" "1307" "1308" "1309" "1310" "1311" "1312" "1314" "1315" "1317" "1318" "1319" "1320" "1323" "1324" "1325" "1326" "1405" "1406" "1407" [841] "1408" "1409" "1410" "1411" "1413" "1414" "1415" "1416" "1418" "1419" "1420" "1422" "1423" "1424" "1425" "1426" "1427" "1428" "1429" "1431" "1432" "1434" "1435" "1436" "1437" "1438" "1441" "1442" [869] "1443" "1444" "1445" "1446" "1447" "1448" "1449" "1450" "1452" "1453" "1454" "1455" "1457" "1458" "1459" "1461" "1462" "1463" "1464" "1465" "1466" "1467" "1468" "1470" "1471" "1473" "1474" "1475" [897] "1476" "1477" "1480" "1481" "1482" "1483" "1484" "1485" "1486" "1487" "1488" "1489" "1491" "1492" "1493" "1494" "1496" "1497" "1498" "1500" "1501" "1502" "1503" "1504" "1505" "1506" "1507" "1509" [925] "1510" "1512" "1513" "1514" "1515" "1516" "1519" "1520" "1521" "1522" "1523" "1524" "1525" "1526" "1527" "1528" "1530" "1531" "1532" "1533" "1535" "1536" "1537" "1539" "1540" "1541" "1542" "1543" [953] "1544" "1545" "1546" "1548" "1549" "1551" "1552" "1553" "1554" "1555" "1558" "1559" "1560" attr(,"weights") 1 2 3 4 5 6 8 9 10 11 13 14 15 17 18 1.090510e-04 1.036199e-04 1.157001e-04 1.230697e-04 1.108194e-04 2.236559e-04 1.093574e-04 1.064902e-04 1.081204e-04 9.838923e-05 1.154686e-04 1.022257e-04 1.015956e-04 1.114811e-04 3.954631e-04 19 20 21 22 23 24 26 27 29 30 31 32 33 36 37 1.916271e-04 9.957857e-05 1.071044e-04 2.407185e-04 2.310149e-04 1.143827e-04 1.146863e-04 1.295736e-04 1.042921e-04 1.116676e-04 9.486016e-05 1.156973e-04 1.296313e-04 1.108663e-04 1.217353e-04 38 39 40 42 43 44 45 47 49 54 56 57 58 61 62 2.726271e-04 1.158931e-04 1.090510e-04 2.660482e-03 1.932559e-04 2.705124e-04 2.371664e-04 1.115500e-02 2.784282e-03 1.903493e-03 9.264738e-03 1.690057e-04 3.171291e-04 2.022329e-04 2.198777e-04 63 65 66 69 71 72 75 76 77 78 79 81 82 83 84 2.512769e-03 2.132405e-03 1.816743e-04 5.077173e-03 3.115194e-03 8.805375e-04 2.014264e-03 1.445521e-03 2.018032e-04 2.306447e-04 1.036199e-04 1.296027e-03 2.033412e-04 3.106021e-04 2.228744e-04 86 88 93 95 96 97 100 101 102 104 105 108 110 111 114 3.280581e-03 3.678327e-03 6.402822e-03 2.161195e-03 1.560716e-04 2.759746e-04 1.957950e-04 2.099727e-04 1.746277e-03 1.652134e-03 2.009026e-04 2.386302e-03 1.410139e-03 7.202730e-04 2.169471e-03 115 116 117 118 119 120 121 122 123 125 126 127 128 130 131 8.974985e-04 1.897796e-04 2.606522e-04 1.157001e-04 2.660482e-03 1.296027e-03 1.850962e-04 2.349620e-04 2.498851e-04 2.326323e-03 1.597779e-03 1.340037e-03 9.612714e-04 1.114893e-03 1.297775e-03 132 134 135 136 137 138 139 140 141 143 144 146 147 148 149 1.012536e-03 3.882673e-03 1.848962e-04 3.734809e-04 8.476655e-04 2.290105e-03 2.063720e-04 2.275093e-04 1.740773e-03 1.515159e-03 1.637564e-04 1.290021e-03 2.276730e-03 7.556594e-04 7.980618e-03 150 153 154 155 156 157 158 159 160 161 162 164 165 166 167 9.384215e-04 1.212365e-03 3.765840e-03 2.138050e-04 2.035218e-04 1.230697e-04 1.932559e-04 2.033412e-04 1.850962e-04 2.202142e-04 9.732345e-05 1.899574e-04 1.952065e-04 1.865134e-04 2.409299e-04 169 170 171 173 174 175 176 177 178 179 180 182 183 185 186 1.672889e-04 2.137518e-04 2.068933e-04 1.882965e-04 1.058098e-04 1.240522e-04 2.121261e-04 2.066556e-04 9.270466e-05 9.383486e-05 1.737069e-04 1.717669e-04 2.481713e-04 1.999709e-04 1.826129e-04 187 188 189 192 193 194 195 196 197 198 199 200 201 203 204 2.568993e-04 1.814073e-04 1.482541e-04 1.779472e-04 1.771620e-04 9.108502e-05 2.318142e-04 1.108194e-04 2.705124e-04 3.106021e-04 2.349620e-04 2.202142e-04 1.732005e-04 2.761507e-04 2.957227e-04 205 206 208 209 210 212 213 214 215 216 217 218 219 221 222 3.068843e-04 2.968529e-04 2.947476e-04 3.003234e-04 3.361889e-04 2.594976e-04 1.145620e-04 1.467216e-04 3.609611e-04 2.620049e-04 1.869534e-04 1.810574e-04 2.752284e-04 2.801488e-04 6.175906e-04 224 225 226 227 228 231 232 233 234 235 236 237 238 239 240 3.123333e-04 2.734035e-04 3.184897e-04 2.416662e-04 2.409990e-04 3.061090e-04 2.153252e-04 1.544087e-04 1.101351e-03 2.236559e-04 2.371664e-04 2.228744e-04 2.498851e-04 9.732345e-05 1.732005e-04 242 243 244 245 247 248 249 251 252 253 254 255 256 257 258 2.420155e-04 2.336975e-04 2.462633e-04 1.906321e-04 2.869512e-04 2.120309e-04 2.178353e-04 2.449199e-04 4.337757e-04 4.911022e-04 2.114674e-04 2.199686e-04 1.168350e-03 2.134003e-03 2.726622e-04 260 261 263 264 265 266 267 270 271 272 273 313 314 315 316 2.769120e-04 1.418444e-04 2.268555e-04 2.544896e-04 1.810655e-04 2.569208e-04 3.603239e-04 2.617078e-04 2.636505e-04 1.613341e-03 1.598541e-04 1.093574e-04 1.115500e-02 3.280581e-03 2.326323e-03 317 318 319 321 322 323 325 326 327 329 330 331 332 333 334 1.899574e-04 2.761507e-04 2.420155e-04 5.872301e-03 3.708312e-03 1.276511e-03 1.751926e-03 2.447171e-03 2.035843e-03 6.998005e-03 1.693686e-04 3.172291e-04 1.499223e-03 2.890885e-03 2.067953e-04 335 336 338 339 341 342 343 344 345 348 349 350 351 352 355 2.247549e-04 3.178249e-03 2.642994e-03 1.838645e-04 3.442503e-03 7.577329e-03 9.847603e-04 2.871506e-03 9.396129e-04 2.523458e-03 1.352084e-03 2.049785e-04 2.344897e-04 1.064902e-04 1.597779e-03 356 357 358 360 361 366 368 369 370 373 374 375 377 378 381 1.952065e-04 2.957227e-04 2.336975e-04 5.872301e-03 5.197930e-03 3.254294e-03 3.110092e-03 1.624070e-04 2.944789e-04 2.028619e-04 2.188766e-04 2.445739e-03 2.226535e-03 1.933196e-04 3.852715e-03 383 384 387 388 389 390 391 392 393 394 395 396 397 399 400 1.821441e-03 8.322084e-04 2.774312e-03 1.049940e-03 1.981197e-04 2.491584e-04 1.081204e-04 2.784282e-03 3.678327e-03 1.340037e-03 1.865134e-04 3.068843e-04 2.462633e-04 3.708312e-03 5.197930e-03 401 403 404 405 407 408 409 410 411 412 413 414 416 417 419 1.112278e-03 2.573039e-03 2.077525e-03 2.805069e-03 2.305213e-03 1.645883e-04 2.975446e-04 2.032142e-03 1.631859e-03 2.145984e-04 2.314550e-04 3.327401e-03 3.277737e-03 1.971473e-04 4.697667e-03 420 421 422 423 426 427 428 429 430 433 434 435 436 438 440 3.826120e-03 9.336570e-04 1.578788e-03 9.435685e-04 6.373428e-03 9.495898e-04 2.066570e-04 2.563014e-04 9.838923e-05 9.612714e-04 2.409299e-04 2.968529e-04 1.906321e-04 1.276511e-03 1.112278e-03 444 446 447 448 451 452 453 455 456 459 461 462 465 466 467 1.684812e-03 1.178843e-03 1.452024e-04 2.472980e-04 1.684416e-04 1.796414e-04 8.500211e-04 8.101155e-04 2.013763e-04 1.051156e-03 9.369295e-04 4.942764e-04 8.970394e-04 7.326754e-04 1.661250e-04 468 508 511 512 513 514 516 518 522 524 525 526 529 530 531 2.552516e-04 1.154686e-04 1.114893e-03 1.672889e-04 2.947476e-04 2.869512e-04 1.751926e-03 2.573039e-03 1.258518e-03 1.501633e-03 1.786229e-04 3.321591e-04 2.461782e-04 2.683510e-04 3.963095e-03 533 534 537 539 540 543 544 545 546 547 550 551 552 553 555 5.636015e-03 1.906111e-04 2.265586e-03 1.336947e-03 1.451567e-03 4.540213e-03 9.014433e-04 2.351367e-04 2.464659e-04 1.022257e-04 1.297775e-03 2.137518e-04 3.003234e-04 2.120309e-04 2.447171e-03 557 561 563 564 565 568 569 570 572 573 576 578 579 582 583 2.077525e-03 3.549733e-03 1.948279e-03 1.536400e-04 2.703836e-04 1.859325e-04 1.993399e-04 1.336845e-03 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9.109480e-04 2.174688e-04 2.551195e-04 1.217353e-04 1445 1446 1447 1448 1449 1450 1452 1453 1454 1455 1457 1458 1459 1461 1462 1.445521e-03 8.974985e-04 3.765840e-03 1.771620e-04 2.153252e-04 2.636505e-04 1.352084e-03 1.049940e-03 9.495898e-04 7.326754e-04 9.014433e-04 9.006050e-04 7.473230e-04 1.803487e-03 1.996346e-04 1463 1464 1465 1466 1467 1468 1470 1471 1473 1474 1475 1476 1477 1480 1481 4.312359e-04 6.501310e-04 1.362534e-03 2.127156e-04 2.368727e-04 1.249962e-03 1.136341e-03 1.530800e-04 8.974923e-04 1.396066e-03 6.023923e-04 2.945785e-03 9.244888e-04 9.109480e-04 2.264283e-04 1482 1483 1484 1485 1486 1487 1488 1489 1491 1492 1493 1494 1496 1497 1498 1.880733e-04 2.726271e-04 2.018032e-04 1.897796e-04 2.138050e-04 9.108502e-05 1.544087e-04 1.613341e-03 2.049785e-04 1.981197e-04 2.066570e-04 1.661250e-04 2.351367e-04 1.821555e-04 1.854888e-04 1500 1501 1502 1503 1504 1505 1506 1507 1509 1510 1512 1513 1514 1515 1516 2.080860e-04 5.896430e-04 4.532169e-04 1.803995e-04 1.897293e-04 1.129972e-03 1.577235e-03 2.265132e-04 2.291180e-04 1.330513e-04 1.926181e-04 2.139942e-04 1.582192e-04 2.181942e-04 2.879361e-04 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1530 1531 1532 1533 1535 2.174688e-04 2.264283e-04 1.452722e-04 1.158931e-04 2.306447e-04 2.606522e-04 2.035218e-04 2.318142e-04 1.101351e-03 1.598541e-04 2.344897e-04 2.491584e-04 2.563014e-04 2.552516e-04 2.464659e-04 1536 1537 1539 1540 1541 1542 1543 1544 1545 1546 1548 1549 1551 1552 1553 2.544817e-04 2.791732e-04 2.221146e-04 1.096353e-04 1.329548e-04 2.971101e-04 2.255494e-04 1.759502e-04 1.684760e-04 2.326625e-04 2.361323e-04 9.046081e-04 2.615082e-04 2.319137e-04 2.734177e-04 1554 1555 1558 1559 1560 2.083354e-04 2.073243e-04 2.551195e-04 1.880733e-04 1.452722e-04

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1.283510e-03 1.795060e-04 2.075491e-03 2.127035e-03 6.961767e-04 1.272393e-03 1.362534e-03 1.897293e-04 2.255494e-04 2.407185e-04 2.022331e-04 1.957950e-04 2.063720e-04 863 864 865 867 868 869 870 872 873 874 876 877 878 879 880 9.270466e-05 1.869534e-04 1.168350e-03 2.067953e-04 2.028619e-04 2.145984e-04 1.684416e-04 2.461785e-04 1.859325e-04 1.942486e-04 2.065639e-04 3.622233e-04 3.263473e-04 1.910176e-04 1.879639e-04 881 882 884 885 887 888 889 890 891 894 895 896 897 898 899 2.699481e-03 2.305971e-04 2.349213e-04 1.612519e-04 1.992644e-04 2.161054e-04 1.623312e-04 2.125953e-04 2.885482e-04 2.273510e-04 2.127156e-04 1.129972e-03 1.759502e-04 2.310149e-04 2.198780e-04 900 901 902 903 904 906 907 908 909 911 912 913 915 916 917 2.099727e-04 2.275093e-04 9.383486e-05 1.810574e-04 2.134003e-03 2.247549e-04 2.188766e-04 2.314550e-04 1.796414e-04 2.683510e-04 1.993399e-04 2.068862e-04 2.257500e-04 3.951144e-04 3.927703e-04 918 919 920 921 923 924 926 927 928 929 930 933 934 935 936 2.021812e-04 2.038529e-04 2.699481e-03 2.524382e-04 2.569656e-04 1.516142e-04 2.137890e-04 2.357553e-04 1.718799e-04 2.343566e-04 3.259331e-04 2.459124e-04 2.368727e-04 1.577235e-03 1.684760e-04 937 938 939 940 941 942 943 945 946 947 948 950 951 952 954 1.143827e-04 2.512769e-03 1.746277e-03 1.740773e-03 1.737069e-04 2.752284e-04 2.726622e-04 3.178249e-03 2.445739e-03 3.327401e-03 8.500211e-04 3.963095e-03 1.336845e-03 1.406728e-03 2.736024e-03 955 956 957 958 959 960 962 963 965 966 967 968 969 972 973 1.793704e-04 3.444551e-04 1.147870e-03 1.423550e-03 2.305971e-04 2.524382e-04 1.412412e-02 1.819481e-04 1.917159e-03 5.473218e-03 7.135546e-04 2.288600e-03 1.419902e-03 3.993091e-03 1.249962e-03 974 975 1015 1016 1017 1018 1019 1020 1021 1023 1024 1025 1026 1028 1029 2.265132e-04 2.326625e-04 1.146863e-04 2.132405e-03 1.652134e-03 1.515159e-03 1.717669e-04 2.801488e-04 2.769120e-04 2.642994e-03 2.226535e-03 3.277737e-03 8.101155e-04 5.636015e-03 1.255669e-03 1030 1032 1033 1034 1035 1036 1037 1038 1039 1043 1044 1045 1046 1047 1050 1.378813e-03 2.247863e-03 1.793313e-04 3.419275e-04 1.141437e-03 1.283510e-03 2.349213e-04 2.569656e-04 1.412412e-02 1.823741e-03 4.073893e-03 6.895699e-04 1.928085e-03 1.475112e-03 4.661040e-03 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1062 1063 1064 1065 1067 1.136341e-03 2.291180e-04 2.361323e-04 1.295736e-04 1.816743e-04 2.009026e-04 1.637564e-04 2.481713e-04 6.175906e-04 1.418444e-04 1.838645e-04 1.933196e-04 1.971473e-04 2.013763e-04 1.906111e-04 1068 1069 1071 1072 1073 1074 1075 1076 1077 1078 1080 1082 1083 1084 1085 1.980997e-04 2.122558e-04 1.759473e-04 1.039621e-04 1.134800e-04 2.231638e-04 1.795060e-04 1.612519e-04 1.516142e-04 1.819481e-04 1.841015e-04 2.011565e-04 1.818352e-04 2.142641e-04 1.666744e-04 1086 1089 1090 1091 1092 1132 1135 1136 1137 1138 1140 1142 1147 1149 1150 1.661158e-04 1.961277e-04 1.530800e-04 1.330513e-04 9.046081e-04 1.042921e-04 1.290021e-03 1.999709e-04 3.123333e-04 2.268555e-04 3.442503e-03 4.697667e-03 6.095095e-03 2.181205e-03 1.572235e-04 1151 1154 1155 1156 1158 1159 1161 1163 1164 1167 1168 1169 1170 1171 1172 2.786235e-04 1.992644e-04 2.137890e-04 1.917159e-03 1.823741e-03 2.011565e-04 2.587700e-03 1.426146e-03 7.520152e-04 2.523339e-03 8.974923e-04 1.926181e-04 2.615082e-04 1.116676e-04 5.077173e-03 1173 1174 1175 1176 1177 1179 1180 1181 1182 1184 1185 1186 1188 1189 1190 2.386302e-03 2.276730e-03 1.826129e-04 2.734035e-04 2.544896e-04 7.577329e-03 3.852715e-03 3.826120e-03 1.051156e-03 2.265586e-03 1.804590e-03 1.697901e-03 5.404101e-03 1.742246e-04 3.314651e-04 1191 1192 1193 1194 1195 1197 1198 1200 1201 1202 1203 1206 1207 1208 1209 1.312555e-03 2.075491e-03 2.161054e-04 2.357553e-04 5.473218e-03 4.073893e-03 1.818352e-04 2.587700e-03 8.443986e-04 3.061450e-03 1.108800e-03 3.080008e-03 1.396066e-03 2.139942e-04 2.319137e-04 1210 1213 1214 1215 1216 1218 1220 1225 1227 1228 1229 1232 1233 1234 1236 9.486016e-05 7.556594e-04 2.568993e-04 3.184897e-04 1.810655e-04 9.847603e-04 9.336570e-04 1.465599e-03 9.036082e-04 1.379119e-04 2.282185e-04 1.623312e-04 1.718799e-04 7.135546e-04 6.895699e-04 1237 1240 1241 1242 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 2.142641e-04 8.443986e-04 7.440876e-04 4.393059e-04 7.759423e-04 6.023923e-04 1.582192e-04 2.734177e-04 1.156973e-04 3.115194e-03 1.410139e-03 7.980618e-03 1.814073e-04 2.416662e-04 2.569208e-04 1257 1258 1259 1260 1262 1263 1264 1266 1267 1268 1269 1270 1271 1272 1273 2.871506e-03 1.821441e-03 1.578788e-03 9.369295e-04 1.336947e-03 1.334488e-03 1.088982e-03 5.170104e-03 1.848693e-04 3.726899e-04 9.012872e-04 2.127035e-03 2.125953e-04 2.343566e-04 2.288600e-03 1275 1276 1278 1279 1280 1281 1284 1285 1286 1287 1288 1289 1290 1291 1292 1.928085e-03 1.666744e-04 1.426146e-03 3.061450e-03 7.440876e-04 1.064315e-03 1.445113e-03 2.945785e-03 2.181942e-04 2.083354e-04 1.296313e-04 8.805375e-04 7.202730e-04 9.384215e-04 1.482541e-04 1293 1294 1296 1297 1298 1299 1301 1302 1303 1305 1306 1307 1308 1309 1310 2.409990e-04 3.603239e-04 9.396129e-04 8.322084e-04 9.435685e-04 4.942764e-04 1.451567e-03 6.370484e-04 6.559482e-04 9.644626e-04 2.132739e-04 4.398415e-04 5.934697e-04 6.961767e-04 2.885482e-04 1311 1312 1314 1315 1317 1318 1319 1320 1323 1324 1325 1326 1405 1406 1407 3.259331e-04 1.419902e-03 1.475112e-03 1.661158e-04 7.520152e-04 1.108800e-03 4.393059e-04 1.064315e-03 1.099805e-03 9.244888e-04 2.879361e-04 2.073243e-04 1.108663e-04 2.014264e-03 2.169471e-03 1408 1409 1410 1411 1413 1414 1415 1416 1418 1419 1420 1422 1423 1424 1425 1.212365e-03 1.779472e-04 3.061090e-04 2.617078e-04 2.523458e-03 2.774312e-03 6.373428e-03 8.970394e-04 4.540213e-03 1.470331e-03 1.899710e-03 1.862354e-03 1.694001e-04 3.083198e-04 1.543383e-03 1426 1427 1428 1429 1431 1432 1434 1435 1436 1437 1438 1441 1442 1443 1444 1.272393e-03 2.273510e-04 2.459124e-04 3.993091e-03 4.661040e-03 1.961277e-04 2.523339e-03 3.080008e-03 7.759423e-04 1.445113e-03 1.099805e-03 9.109480e-04 2.174688e-04 2.551195e-04 1.217353e-04 1445 1446 1447 1448 1449 1450 1452 1453 1454 1455 1457 1458 1459 1461 1462 1.445521e-03 8.974985e-04 3.765840e-03 1.771620e-04 2.153252e-04 2.636505e-04 1.352084e-03 1.049940e-03 9.495898e-04 7.326754e-04 9.014433e-04 9.006050e-04 7.473230e-04 1.803487e-03 1.996346e-04 1463 1464 1465 1466 1467 1468 1470 1471 1473 1474 1475 1476 1477 1480 1481 4.312359e-04 6.501310e-04 1.362534e-03 2.127156e-04 2.368727e-04 1.249962e-03 1.136341e-03 1.530800e-04 8.974923e-04 1.396066e-03 6.023923e-04 2.945785e-03 9.244888e-04 9.109480e-04 2.264283e-04 1482 1483 1484 1485 1486 1487 1488 1489 1491 1492 1493 1494 1496 1497 1498 1.880733e-04 2.726271e-04 2.018032e-04 1.897796e-04 2.138050e-04 9.108502e-05 1.544087e-04 1.613341e-03 2.049785e-04 1.981197e-04 2.066570e-04 1.661250e-04 2.351367e-04 1.821555e-04 1.854888e-04 1500 1501 1502 1503 1504 1505 1506 1507 1509 1510 1512 1513 1514 1515 1516 2.080860e-04 5.896430e-04 4.532169e-04 1.803995e-04 1.897293e-04 1.129972e-03 1.577235e-03 2.265132e-04 2.291180e-04 1.330513e-04 1.926181e-04 2.139942e-04 1.582192e-04 2.181942e-04 2.879361e-04 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1530 1531 1532 1533 1535 2.174688e-04 2.264283e-04 1.452722e-04 1.158931e-04 2.306447e-04 2.606522e-04 2.035218e-04 2.318142e-04 1.101351e-03 1.598541e-04 2.344897e-04 2.491584e-04 2.563014e-04 2.552516e-04 2.464659e-04 1536 1537 1539 1540 1541 1542 1543 1544 1545 1546 1548 1549 1551 1552 1553 2.544817e-04 2.791732e-04 2.221146e-04 1.096353e-04 1.329548e-04 2.971101e-04 2.255494e-04 1.759502e-04 1.684760e-04 2.326625e-04 2.361323e-04 9.046081e-04 2.615082e-04 2.319137e-04 2.734177e-04 1554 1555 1558 1559 1560 2.083354e-04 2.073243e-04 2.551195e-04 1.880733e-04 1.452722e-04

adamrauh commented 4 years ago

I was just suggesting that there might be a bug in the code, which we will of course fix if that is the case. Based on the output you shared, there isn't necessarily a problem, though certainly this is not typical. The weights will depend on the similarity of the control units to the treated unit within each matched set. The weights of a particular control unit across matched sets aren't really related.

How are you specifying refinement? What variables are you using?

einter-dot commented 4 years ago

This is my command (quite standard indeed): PM.results <- PanelMatch(lag = 4, time.id = "year", unit.id = "id", treatment = "t", refinement.method = "CBPS.weight", data = mydata, match.missing = TRUE, covs.formula = ~ loggdp_e + loggdp_i + logdist,
size.match = 3, qoi = "att", outcome.var = "log_y", lead = 0:4, forbid.treatment.reversal = FALSE) I tried different refinement methods (e.g., ps.weight) but the outcome does not change. Indeed, it is quite similar to that obtained by running it with refinement.method = "none" (although in this latter case the weights are of course all the same). This looks quite bizarre, isn't it?

adamrauh commented 4 years ago

Indeed, this looks pretty standard... The results you've posted seem to suggest that one explanation could be that the loggdp_e, loggdp_i, and logdist variables are all identical (or very similar) for the treated units in the year 1999. Could that be the case?

einter-dot commented 4 years ago

Unfortunately, this is not the case. I also tried with different covariates and the outcomes did not change. If I use only one covariate I usually get the following message: Error in reformulate(response = treatment, termlabels = colnames(pooled)[-c(1:3)]) : 'termlabels' must be a character vector of length at least one. Let's hope there is nothing wrong with the code.

adamrauh commented 4 years ago

Yes, this is quite strange. Could you create a reproducible example (or a few examples) to share so I can look at this? I need to be able to recreate these issues for me to figure out what's happening.

einter-dot commented 4 years ago

Of course, I sent it by email. Thanks a lot!

adamrauh commented 4 years ago

Resolved via email:

2) I believe that having identically weighted control units in your results makes sense, given the details of the weighting method and the structure of your data. Essentially, because all treated units received treatment at the same time and are matched with the same controls, the process for calculating propensity scores and weights is identical for each matched set. Section 3.1.4 may provide some helpful detail: http://web.mit.edu/insong/www/pdf/tscs.pdf