Synthetic Control Without Predictor Variables

[Jason Lennard]

Hi,

I am trying to run a synthetic control model using the "synth" command with no predictor variables. I am only trying to match on the dependent variable as in this paper.

The code I'm using is: synth y, trunit(n) trperiod(xxxx), where y is the name of the dependent variable, n is the number of the treated unit and xxxx is the year of treatment.

However, I get the following error: "not a single variable specified. please supply at least a response variable"

Could someone please help?

Thanks

[Dimitriy V. Masterov]

Try something like this, where you specify the annual values to match on directly:

use "smoking.dta"
xtset state year 
synth cigsale cigsale(1984) cigsale(1985) cigsale(1986) cigsale(1987) cigsale(1988), trunit(3) trperiod(1989)

[Olivia Sun]

Hi, I have the same problem and have tried specifying annual values but it says expression too long? What is the solution? Thanks!

[Jared Greathouse]

Hey I'm the resident expert on SCM. Show me the code you used, tell me how many donors you have.

[Jane Quan]

Jared Greathouse
I'm facing an issue similar to the one discussed in this post.
I am trying to use one dependent variable (price) with the "synth2" command. My dataset is monthly and spans from 200501 to 202405. My treatment variable is #1, and I have 35 control variables to use as a donor pool.
When I run the following code on a different dataset, it seems to work fine. However, with this particular dataset, I encounter a conformability error. While I understand what a conformability error means in general, I'm not sure where the problem lies in this case.
I'd appreciate any insights or guidance you can provide. Thanks in advance for your help!

(case1) Invalid syntax:

synth2 price, trunit(1) trperiod(202307)

(case2) Conformability error:

synth2 price item, trunit(1) trperiod(202307)

* Example generated by -dataex-. For more info, type help dataex
clear
input long(ym item) double price
200501  3  96.11829944547135
200501 19 115.39736405966379
200501 18  141.4827750436577
200501 33   95.1219512195122
200501 17 102.80437756497949
200501 25  94.76013479310993
200501 20  87.56832515767344
200501 16  78.91934529678562
200501 10  86.16975441619992
200501 11  99.34235328613916
200501 23  35.49595740485111
200501  2  87.25463865762096
200501 30  72.81448614941837
200501 24  97.34620024125452
200501  6  91.34582623509367
200501 12 101.98300283286122
200501  9  83.01325964284885
200501  1  97.73298846980433
200501 27  81.56965766357439
200501  5  98.46728971962617
200501 15  105.0363686585029
200501 26  95.22960320998662
200501 31  92.74965800273598
200501  4   102.236709782571
200501 22  91.48468794417884
200501 29  74.99520981030848
200501 35  77.30128713475264
200501 13  98.95672920507536
200501  8  98.41376982787715
200501 21  96.52840396753832
200501  7 114.00522523949014
200501 34    93.755990919028
200501 14 127.60602374260665
200501 28 197.55955839628123
200501 32 121.00527458889232
200502 13 100.67454722915086
200502 24   78.1664656212304
200502  9  95.88742075563708
200502 28 197.55955839628123
200502 18 115.63740276234323
200502 16  78.91934529678562
200502  6  94.54855195911412
200502 31 105.06155950752394
200502 21  98.70754433423504
200502 22  62.02351725029074
200502 23  85.42693748767502
200502  5 102.72897196261684
200502 10  99.09521757862991
200502  8  98.00877489031387
200502 25  99.79318224549122
200502 14  80.54029111959395
200502 15  105.0363686585029
200502  7 114.00522523949014
200502  2  83.47131563890284
200502 11  99.52666191932309
200502 35  72.17714878301713
200502 27  82.06222806009357
200502 12 101.98300283286122
200502 33  94.57317073170732
200502  4 102.24364531678052
200502 32 105.64691281414831
200502  1  99.38252616602999
200502  3 102.77264325323475
200502 20  91.32445690259286
200502 34  96.19776339022955
200502 29  80.45602605863192
200502 17 101.77838577291382
200502 19 115.39736405966379
200502 30  51.08526202182515
200502 26 103.25456977262594
200503  1   99.9921383456818
200503  4  100.4473419565142
200503  9  99.06416180944197
200503 21  98.70754433423504
200503 26 105.12706197057513
200503  5 102.50467289719627
200503  8  97.19878501518731
200503 13 101.71564906192387
200503 20 106.06867554309743
200503  2     77.30590034914
200503 10  94.65747522619561
200503 15  105.0363686585029
200503 22 59.232458974027644
200503 18 116.65343705350055
200503 33  96.76829268292684
200503 30  55.40232641803573
200503 29  83.34930063230503
200503 19 115.39736405966379
200503 25  98.92541544335651
200503 31 111.62790697674419
200503 24  87.57539203860073
200503 32 103.04064536146447
200503 17 101.98358413132695
200503 28 197.55955839628123
200503  3 109.42698706099814
200503 12 101.98300283286122
200503 11 101.18543961797847
200503  6  99.99999999999999
200503 27  80.28897463262456
200503 14  62.92210243718276
200503 34 100.35483057260572
200503  7 114.00522523949014
200503 35  83.81626303910204
200503 16  78.91934529678562
200503 23  79.74758430289884
200504 18  83.69582473408475
200504 10  87.03145196036192
200504 16  78.91934529678562
200504 30  76.05228444657632
200504  2  75.40022653230422
200504 14  54.51570955157514
200504  4  101.7442868536949
200504 24  86.48974668275031
200504 20 109.93693062368605
200504  5  99.36448598130842
200504 27   80.0919464740169
200504  7  64.93547620932625
200504  6  99.99999999999999
200504 23    74.304870834155
200504 22  93.03527587543611
200504 28 101.49138098005035
200504  8 100.03374957813027
200504  3 108.68761552680222
200504 32 104.52994104871239
200504  1 100.48253758269769
200504 33   95.1219512195122
200504 11 102.45884471997655
200504 34 104.26973850163964
200504 31 105.06155950752394
200504 29  85.61027016669858
200504 35 108.55853108033917
200504 17   99.1108071135431
200504 15  105.0363686585029
200504 25 101.23367513703482
200504  9 102.85395815433202
200504 21  98.52720168319807
200504 13 102.97798503416115
200504 26  97.36959429335712
200504 12 101.50708215297453
200504 19  63.92549917133611
200505 15  84.15431359911611
200505 32  94.47719515978902
200505 11  101.5373015540569
200505 31   90.2872777017784
200505 28  58.02827813286848
200505 20 109.93693062368605
200505  6  99.99999999999999
200505  4 101.32815480112356
200505  2  94.10665701374374
200505 19  57.86441480546129
200505 16 124.82745020705974
200505 29  89.13584977965128
200505 10  88.75484704868593
200505 35  98.09064844750807
200505  9 105.97496690894737
200505 12  106.1303116147309
200505 18  74.67852040006349
200505  8 102.86871414107324
200505 25 100.66094904762592
200505 14  81.54704475858885
200505 26  96.56709763709318
200505  7    78.188583643417
200505 21   97.2497745716862
200505 23 150.02957996450405
200505 13 101.10400173516972
200505  1  99.12486721211037
200505  3  104.2513863216266
200505 27   80.0919464740169
200505 22  96.13645173795065
200505 30  97.78150857416955
200505 34 105.98503321281427
200505  5  96.00000000000001
200505 17 103.00957592339262
200505 33   95.1219512195122
200505 24  89.02291917973463
200506 34 103.82577987051206
200506  4  97.31248049381004
200506 29 100.05748227629813
200506 13   99.2950873007266
200506  5  90.16822429906543
200506 30  97.78150857416955
200506 17  98.70041039671682
200506  6  99.99999999999999
200506  2 104.32162916428265
200506  1  99.73667223259697
200506 10   90.8229211546747
200506 15   85.9957646625541
200506 25 100.66094904762592
200506 32  94.47719515978902
200506 31  82.07934336525308
200506 12 110.27762039660058
200506 18  76.20257183679948
200506  3  96.11829944547135
200506  7  79.66115113609375
200506 14 125.18981500901883
200506 33   95.1219512195122
200506 11 101.16868428768902
200506 23  155.4722934332479
200506 28  62.67673833042804
200506 26  98.70708872046366
end
label values item item
label def item 1 " Paddy", modify
label def item 2 "Banana, Cavendish", modify
label def item 3 "Cassava", modify
label def item 4 "Cattle", modify
label def item 5 "Coconut", modify
label def item 6 "Coffee", modify
label def item 7 "Durain", modify
label def item 8 "Egg, chicken", modify
label def item 9 "Fowl", modify
label def item 10 "Garlic", modify
label def item 11 "Glutinous paddy", modify
label def item 12 "Groundnut", modify
label def item 13 "Hommali paddy", modify
label def item 14 "Longan", modify
label def item 15 "Longkong", modify
label def item 16 "Lychee", modify
label def item 17 "Maize", modify
label def item 18 "Mango, Khiaw Sawei", modify
label def item 19 "Mangosteen", modify
label def item 20 "Mung bean", modify
label def item 21 "Non-Glutinous paddy", modify
label def item 22 "Onion", modify
label def item 23 "Orchids", modify
label def item 24 "Palm kernel (weight over 15 kg.)", modify
label def item 25 "Peppercorn", modify
label def item 26 "Pineapple", modify
label def item 27 "Potato", modify
label def item 28 "Rambutan", modify
label def item 29 "Rubber, un-smoked sheet 3", modify
label def item 30 "Shallot", modify
label def item 31 "Shrimp, Vannamei", modify
label def item 32 "Soybean", modify
label def item 33 "Sugarcane", modify
label def item 34 "Swine (weight over 100 kg.)", modify
label def item 35 "Tangerine", modify

[Jared Greathouse]

Hey what's up Jane Quan , the example you've given me is an unbalanced panel. For SCM to work, we need a balanced panel dataset where we observe all units across all time points. For example, when I do

* Example generated by -dataex-. For more info, type help dataex
clear
input long(ym item) double price
200501  3  96.11829944547135
200501 19 115.39736405966379
200501 18  141.4827750436577
200501 33   95.1219512195122
200501 17 102.80437756497949
200501 25  94.76013479310993
200501 20  87.56832515767344
200501 16  78.91934529678562
200501 10  86.16975441619992
200501 11  99.34235328613916
200501 23  35.49595740485111
200501  2  87.25463865762096
200501 30  72.81448614941837
200501 24  97.34620024125452
200501  6  91.34582623509367
200501 12 101.98300283286122
200501  9  83.01325964284885
200501  1  97.73298846980433
200501 27  81.56965766357439
200501  5  98.46728971962617
200501 15  105.0363686585029
200501 26  95.22960320998662
200501 31  92.74965800273598
200501  4   102.236709782571
200501 22  91.48468794417884
200501 29  74.99520981030848
200501 35  77.30128713475264
200501 13  98.95672920507536
200501  8  98.41376982787715
200501 21  96.52840396753832
200501  7 114.00522523949014
200501 34    93.755990919028
200501 14 127.60602374260665
200501 28 197.55955839628123
200501 32 121.00527458889232
200502 13 100.67454722915086
200502 24   78.1664656212304
200502  9  95.88742075563708
200502 28 197.55955839628123
200502 18 115.63740276234323
200502 16  78.91934529678562
200502  6  94.54855195911412
200502 31 105.06155950752394
200502 21  98.70754433423504
200502 22  62.02351725029074
200502 23  85.42693748767502
200502  5 102.72897196261684
200502 10  99.09521757862991
200502  8  98.00877489031387
200502 25  99.79318224549122
200502 14  80.54029111959395
200502 15  105.0363686585029
200502  7 114.00522523949014
200502  2  83.47131563890284
200502 11  99.52666191932309
200502 35  72.17714878301713
200502 27  82.06222806009357
200502 12 101.98300283286122
200502 33  94.57317073170732
200502  4 102.24364531678052
200502 32 105.64691281414831
200502  1  99.38252616602999
200502  3 102.77264325323475
200502 20  91.32445690259286
200502 34  96.19776339022955
200502 29  80.45602605863192
200502 17 101.77838577291382
200502 19 115.39736405966379
200502 30  51.08526202182515
200502 26 103.25456977262594
200503  1   99.9921383456818
200503  4  100.4473419565142
200503  9  99.06416180944197
200503 21  98.70754433423504
200503 26 105.12706197057513
200503  5 102.50467289719627
200503  8  97.19878501518731
200503 13 101.71564906192387
200503 20 106.06867554309743
200503  2     77.30590034914
200503 10  94.65747522619561
200503 15  105.0363686585029
200503 22 59.232458974027644
200503 18 116.65343705350055
200503 33  96.76829268292684
200503 30  55.40232641803573
200503 29  83.34930063230503
200503 19 115.39736405966379
200503 25  98.92541544335651
200503 31 111.62790697674419
200503 24  87.57539203860073
200503 32 103.04064536146447
200503 17 101.98358413132695
200503 28 197.55955839628123
200503  3 109.42698706099814
200503 12 101.98300283286122
200503 11 101.18543961797847
200503  6  99.99999999999999
200503 27  80.28897463262456
200503 14  62.92210243718276
200503 34 100.35483057260572
200503  7 114.00522523949014
200503 35  83.81626303910204
200503 16  78.91934529678562
200503 23  79.74758430289884
200504 18  83.69582473408475
200504 10  87.03145196036192
200504 16  78.91934529678562
200504 30  76.05228444657632
200504  2  75.40022653230422
200504 14  54.51570955157514
200504  4  101.7442868536949
200504 24  86.48974668275031
200504 20 109.93693062368605
200504  5  99.36448598130842
200504 27   80.0919464740169
200504  7  64.93547620932625
200504  6  99.99999999999999
200504 23    74.304870834155
200504 22  93.03527587543611
200504 28 101.49138098005035
200504  8 100.03374957813027
200504  3 108.68761552680222
200504 32 104.52994104871239
200504  1 100.48253758269769
200504 33   95.1219512195122
200504 11 102.45884471997655
200504 34 104.26973850163964
200504 31 105.06155950752394
200504 29  85.61027016669858
200504 35 108.55853108033917
200504 17   99.1108071135431
200504 15  105.0363686585029
200504 25 101.23367513703482
200504  9 102.85395815433202
200504 21  98.52720168319807
200504 13 102.97798503416115
200504 26  97.36959429335712
200504 12 101.50708215297453
200504 19  63.92549917133611
200505 15  84.15431359911611
200505 32  94.47719515978902
200505 11  101.5373015540569
200505 31   90.2872777017784
200505 28  58.02827813286848
200505 20 109.93693062368605
200505  6  99.99999999999999
200505  4 101.32815480112356
200505  2  94.10665701374374
200505 19  57.86441480546129
200505 16 124.82745020705974
200505 29  89.13584977965128
200505 10  88.75484704868593
200505 35  98.09064844750807
200505  9 105.97496690894737
200505 12  106.1303116147309
200505 18  74.67852040006349
200505  8 102.86871414107324
200505 25 100.66094904762592
200505 14  81.54704475858885
200505 26  96.56709763709318
200505  7    78.188583643417
200505 21   97.2497745716862
200505 23 150.02957996450405
200505 13 101.10400173516972
200505  1  99.12486721211037
200505  3  104.2513863216266
200505 27   80.0919464740169
200505 22  96.13645173795065
200505 30  97.78150857416955
200505 34 105.98503321281427
200505  5  96.00000000000001
200505 17 103.00957592339262
200505 33   95.1219512195122
200505 24  89.02291917973463
200506 34 103.82577987051206
200506  4  97.31248049381004
200506 29 100.05748227629813
200506 13   99.2950873007266
200506  5  90.16822429906543
200506 30  97.78150857416955
200506 17  98.70041039671682
200506  6  99.99999999999999
200506  2 104.32162916428265
200506  1  99.73667223259697
200506 10   90.8229211546747
200506 15   85.9957646625541
200506 25 100.66094904762592
200506 32  94.47719515978902
200506 31  82.07934336525308
200506 12 110.27762039660058
200506 18  76.20257183679948
200506  3  96.11829944547135
200506  7  79.66115113609375
200506 14 125.18981500901883
200506 33   95.1219512195122
200506 11 101.16868428768902
200506 23  155.4722934332479
200506 28  62.67673833042804
200506 26  98.70708872046366
end
label values item item
label def item 1 " Paddy", modify
label def item 2 "Banana, Cavendish", modify
label def item 3 "Cassava", modify
label def item 4 "Cattle", modify
label def item 5 "Coconut", modify
label def item 6 "Coffee", modify
label def item 7 "Durain", modify
label def item 8 "Egg, chicken", modify
label def item 9 "Fowl", modify
label def item 10 "Garlic", modify
label def item 11 "Glutinous paddy", modify
label def item 12 "Groundnut", modify
label def item 13 "Hommali paddy", modify
label def item 14 "Longan", modify
label def item 15 "Longkong", modify
label def item 16 "Lychee", modify
label def item 17 "Maize", modify
label def item 18 "Mango, Khiaw Sawei", modify
label def item 19 "Mangosteen", modify
label def item 20 "Mung bean", modify
label def item 21 "Non-Glutinous paddy", modify
label def item 22 "Onion", modify
label def item 23 "Orchids", modify
label def item 24 "Palm kernel (weight over 15 kg.)", modify
label def item 25 "Peppercorn", modify
label def item 26 "Pineapple", modify
label def item 27 "Potato", modify
label def item 28 "Rambutan", modify
label def item 29 "Rubber, un-smoked sheet 3", modify
label def item 30 "Shallot", modify
label def item 31 "Shrimp, Vannamei", modify
label def item 32 "Soybean", modify
label def item 33 "Sugarcane", modify
label def item 34 "Swine (weight over 100 kg.)", modify
label def item 35 "Tangerine", modify


bys item: g obs = _N

br 

xtset item ym



//synth2 price, trunit(1) trperiod(202307)

I see that Egg and Chicken have 5 observations but garlic has 6. Presumably this is a common issue for all your units of interest. So under the hood, you'll get conformability errors since the OLS underlying SCM can't estimate like that.

[Jane Quan]

Jared Greathouse

Thank you so much for your reply. Please take a look at the following data example; it is balanced data.

* Example generated by -dataex-. For more info, type help dataex
clear
input long(ym item) double price
202207  1  146.9655973076763
202208  1  146.4255188965499
202209  1 147.54164540120252
202210  1 150.14065657428324
202211  1  148.5612931031437
202212  1 146.74505680408345
202301  1 153.85223910321233
202302  1 154.62397822827955
202303  1 154.96173870778233
202304  1  156.3860187176764
202305  1 157.49974027595542
202306  1 159.61159802698538
202307  1  164.5787392071495
202308  1 179.40230370815533
202309  1 192.26385634401257
202310  1 189.79914393898736
202311  1 164.64181143299686
202312  1  162.4698461071535
202401  1  170.6627163216008
202402  1  175.5945125962027
202403  1 171.37154041388482
202404  1   165.669700290087
202405  1 171.13749183055285
202207  2 283.74921931012756
202208  2  294.2584511262393
202209  2  285.5007577009308
202210  2 283.04860536904926
202211  2  278.4946041377178
202212  2  278.4946041377178
202301  2  278.4946041377178
202302  2 274.99152735611125
202303  2   273.239988965308
202304  2 283.74921694555087
202305  2  287.9529128878199
202306  2 289.00383448253734
202307  2  307.1021319025398
202308  2 311.77733663976124
202309  2  319.9500147712492
202310  2  303.7559929440186
202311  2  300.6830924724134
202312  2  311.7738335629797
202401  2 329.28571439423064
202402  2 298.34303718230035
202403  2 311.77733663976124
202404  2  326.3676514351524
202405  2 334.83458801629536
202207  3 200.73936651571174
202208  3 200.36968175970435
202209  3  197.9667233317931
202210  3 192.23659504621082
202211  3 191.12753681146035
202212  3 192.60627984288362
202301  3 199.63030924214436
202302  3  211.4602534935307
202303  3 225.50831792975976
202304  3 232.65556833846819
202305  3 228.61367380110917
202306  3 206.28465288354911
202307  3 202.73567062181158
202308  3 205.36043848890958
202309  3 218.29944001386338
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202212 28  276.2347334282401
202301 28  276.2347334282401
202302 28  276.2347334282401
202303 28  276.2347334282401
202304 28  271.1601510749591
202305 28  286.9184525223321
202306 28  231.1640461287043
202307 28 263.21130598892125
202308 28  222.1189175537479
202309 28 137.30388965281819
202310 28  88.41758446397446
202311 28  88.41758446397446
202312 28  88.41758446397446
202401 28  88.41758446397446
202402 28  88.41758446397446
202403 28  88.41758446397446
202404 28   348.634497385242
202405 28 211.60177661969797
202207 29  107.5062245589434
202208 29  97.96895957079904
202209 29  87.28204636903625
202210 29  89.95976060739612
202211 29  83.10978906998476
202212 29  82.60681916495025
202301 29  83.09541850126949
202302 29  86.57788631993685
202303 29  85.78750504059695
202304 29  83.20239870835209
202305 29  84.58708395685005
202306 29  84.84383769496081
202307 29  82.49472914144478
202308 29   80.4608142368031
202309 29  87.20061096426527
202310 29  93.68844418869523
202311 29  96.18700660088149
202312 29  94.95113769136817
202401 29 108.23912356342221
202402 29 127.97470459906125
202403 29 146.45525596689993
202404 29   142.924886252515
202405 29  137.9382989083159
202207 30 266.61469533727075
202208 30 266.61469533727075
202209 30 266.61469533727075
202210 30 266.61469533727075
202211 30 266.61469533727075
202212 30 361.64407298312165
202301 30  278.0369279333554
202302 30 219.25290254122206
202303 30 215.99711655714123
202304 30  248.5909332893656
202305 30  248.5909332893656
202306 30  248.5909332893656
202307 30  248.5909332893656
202308 30  248.5909332893656
202309 30  248.5909332893656
202310 30  248.5909332893656
202311 30  248.5909332893656
202312 30 374.68520468731276
202401 30  216.6446762003838
202402 30 251.14521497091985
202403 30  263.9345179249611
202404 30 292.79289106767425
202405 30 292.79289106767425
202207 31 123.33857420791729
202208 31 116.19972407819428
202209 31 113.60191234463069
202210 31 107.49767226865119
202211 31 119.83584131326948
202212 31 122.82968229177503
202301 31 134.82557802470933
202302 31 130.51436062222987
202303 31 118.37072207493165
202304 31 109.32968171819437
202305 31 103.65471748914914
202306 31  98.80505908938102
202307 31  93.27496393477432
202308 31  87.06566129805064
202309 31  84.93775437450415
202310 31   90.5926110472449
202311 31 100.86730253987692
202312 31 102.46169374488717
202401 31 104.54650901424422
202402 31 104.64705620735299
202403 31 104.59165265196651
202404 31 104.75375935106024
202405 31 100.92270609526337
202207 32 190.81600515823786
202208 32 190.81600515823786
202209 32 190.81600515823786
202210 32 170.64845847554068
202211 32 176.85385843934242
202212 32  193.1430296113871
202301 32 223.97610361561453
202302 32  243.1740553540958
202303 32 246.19918714318985
202304 32 205.86409490640222
202305 32 200.44057929866602
202306 32 200.12410297083483
202307 32  200.1241039714553
202308 32 222.88240212263443
202309 32  217.6465971030486
202310 32 206.58392388569672
202311 32 152.42010168127536
202312 32 155.91063836099923
202401 32 155.91063836099923
202402 32 155.91063836099923
202403 32 181.04249792894873
202404 32 201.72974984350782
202405 32 184.16071522223106
202207 33 196.25990363008788
202208 33 196.25990363008788
202209 33 196.25990363008788
202210 33 196.25990363008788
202211 33 196.25990363008788
202212 33 206.60761678602918
202301 33 195.87712924941331
202302 33 207.98780487804885
202303 33 212.50975078481727
202304 33 212.50975078481727
202305 33 212.50975078481727
202306 33 212.50975078481727
202307 33 212.50975078481727
202308 33 212.50975078481727
202309 33 212.50975078481727
202310 33 212.50975078481727
202311 33 212.50975078481727
202312 33 242.65791874067116
202401 33 257.09404845069764
202402 33 263.85685315967635
202403 33 273.36996267794615
202404 33 273.36996267794615
202405 33 273.36996267794615
202207 34 208.09551316976385
202208 34 210.17808374733377
202209 34 211.21835846291944
202210 34  209.7341251250854
202211 34 211.55637241225944
202212 34  206.5618379369378
202301 34 202.84368449419839
202302 34 189.45429611288165
202303 34 177.07390459526624
202304 34  175.0071412277252
202305 34 163.96602715875227
202306 34  159.6081688845961
202307 34 146.77675650278994
202308 34  140.2253391328934
202309 34 138.64625904120078
202310 34 129.66013362137733
202311 34 130.51878930408654
202312 34 133.73748929929383
202401 34 136.35583616059876
202402 34 135.64449337168932
202403 34 129.27267817741537
202404 34 132.35011861170443
202405 34 142.61666169986555
202207 35  153.7241428658578
202208 35  153.7241428658578
202209 35  153.7241428658578
202210 35 210.45567704203026
202211 35 190.32513243457598
202212 35 201.30543021411592
202301 35 192.15518338620154
202302 35 161.19074812053935
202303 35 258.98856679314605
202304 35 258.98856679314605
202305 35 215.94582513877884
202306 35 164.70444290245845
202307 35 164.70444290245845
202308 35 164.70444290245845
202309 35 164.70444290245845
202310 35 164.70444290245845
202311 35 164.70444290245845
202312 35 164.70444290245845
202401 35  183.0049228329183
202402 35  183.0049228329183
202403 35  183.0049228329183
202404 35 146.40393826633465
202405 35  164.7044387848476
end
label values item item
label def item 1 " Paddy", modify
label def item 2 "Banana, Cavendish", modify
label def item 3 "Cassava", modify
label def item 4 "Cattle", modify
label def item 5 "Coconut", modify
label def item 6 "Coffee", modify
label def item 7 "Durain", modify
label def item 8 "Egg, chicken", modify
label def item 9 "Fowl", modify
label def item 10 "Garlic", modify
label def item 11 "Glutinous paddy", modify
label def item 12 "Groundnut", modify
label def item 13 "Hommali paddy", modify
label def item 14 "Longan", modify
label def item 15 "Longkong", modify
label def item 16 "Lychee", modify
label def item 17 "Maize", modify
label def item 18 "Mango, Khiaw Sawei", modify
label def item 19 "Mangosteen", modify
label def item 20 "Mung bean", modify
label def item 21 "Non-Glutinous paddy", modify
label def item 22 "Onion", modify
label def item 23 "Orchids", modify
label def item 24 "Palm kernel (weight over 15 kg.)", modify
label def item 25 "Peppercorn", modify
label def item 26 "Pineapple", modify
label def item 27 "Potato", modify
label def item 28 "Rambutan", modify
label def item 29 "Rubber, un-smoked sheet 3", modify
label def item 30 "Shallot", modify
label def item 31 "Shrimp, Vannamei", modify
label def item 32 "Soybean", modify
label def item 33 "Sugarcane", modify
label def item 34 "Swine (weight over 100 kg.)", modify
label def item 35 "Tangerine", modify

[Tshego Mello]

Hi. I have a similar issue.

i checked
bys item: g obs = _N br and i have 40 observations for each unit(country) yet i still have a comfortability error.

code:
. synth GDP_PC GDP_PC(1993) GDP_PC(1992) GDP_PC(1991) GDP_PC(1990) GDP_PC
> (1989) GDP_PC(1988) GDP_PC(1987) GDP_PC(1986) GDP_PC(1985) GDP_PC(1984)
> GDP_PC(1983) GDP_PC(1982) GDP_PC(1981) GDP_PC(1980), trunit(44) trperiod
> (1994) keep("synth_weight.dta") replace

please assist @Jared Greathouse

[Jared Greathouse]

I do not know why synth returns this. However, I have a different recommendation since synth isn't working. One solution is maybe the forward difference-in-differences method. Tshego Mello

[Tshego Mello]

the problem is my paper is based on SCM. Please, what can show you for you to be able to identify the issue? @Jared Greathouse

* Example generated by -dataex-. For more info, type help dataex
clear
input str24 Country int Years double GDP_PC byte Treated long numeric_country
"Algeria" 1980 2259.7243664761 0 1
"Algeria" 1981 2291.75610066667 0 1
"Algeria" 1982 2260.34752384937 0 1
"Algeria" 1983 2359.59326397424 0 1
"Algeria" 1984 2510.03683324232 0 1
"Algeria" 1985 2617.72544861118 0 1
"Algeria" 1986 2783.4310226391 0 1
"Algeria" 1987 2829.87927404758 0 1
"Algeria" 1988 2437.37792301413 0 1
"Algeria" 1989 2235.26812213413 0 1
"Algeria" 1990 2431.55135968865 0 1
"Algeria" 1991 1749.28608748967 0 1
"Algeria" 1992 1794.62350740515 0 1
"Algeria" 1993 1825.87509656701 0 1
"Algeria" 1994 1522.82520284828 0 1
"Algeria" 1995 1466.54468037742 0 1
"Algeria" 1996 1619.53241242785 0 1
"Algeria" 1997 1634.46740988129 0 1
"Algeria" 1998 1610.30297761325 0 1
"Algeria" 1999 1602.8649145582 0 1
"Algeria" 2000 1780.37606280604 0 1
"Algeria" 2001 1904.21555356782 0 1
"Algeria" 2002 1945.19192868032 0 1
"Algeria" 2003 2292.31758148495 0 1
"Algeria" 2004 2827.22716459299 0 1
"Algeria" 2005 3248.09981432319 0 1
"Algeria" 2006 3681.29098817798 0 1
"Algeria" 2007 4192.66316915541 0 1
"Algeria" 2008 5217.99182157355 0 1
"Algeria" 2009 4270.85845146029 0 1
"Algeria" 2010 4958.2593791361 0 1
"Algeria" 2011 5974.57008682611 0 1
"Algeria" 2012 6096.09001549263 0 1
"Algeria" 2013 6044.67490330703 0 1
"Algeria" 2014 6164.64469897525 0 1
"Algeria" 2015 4741.49977033558 0 1
"Algeria" 2016 4481.08196153066 0 1
"Algeria" 2017 4615.86874364885 0 1
"Algeria" 2018 4640.31414537956 0 1
"Algeria" 2019 4530.10174483631 0 1
"Angola" 1980 802.627 0 2
"Angola" 1981 731.427 0 2
"Angola" 1982 712.576 0 2
"Angola" 1983 723.654 0 2
"Angola" 1984 747.325 0 2
"Angola" 1985 817.068 0 2
"Angola" 1986 743.735 0 2
"Angola" 1987 828.958 0 2
"Angola" 1988 875.526 0 2
"Angola" 1989 991.295 0 2
"Angola" 1990 1062.788 0 2
"Angola" 1991 996.472 0 2
"Angola" 1992 743.754 0 2
"Angola" 1993 450.980076344484 0 2
"Angola" 1994 329.224181981965 0 2
"Angola" 1995 398.113614094094 0 2
"Angola" 1996 454.376649770871 0 2
"Angola" 1997 516.128602285455 0 2
"Angola" 1998 423.40371118599 0 2
"Angola" 1999 387.689437952028 0 2
"Angola" 2000 556.884252978488 0 2
"Angola" 2001 527.46411051877 0 2
"Angola" 2002 872.657631363509 0 2
"Angola" 2003 982.805587449171 0 2
"Angola" 2004 1254.696118613 0 2
"Angola" 2005 1900.72380923794 0 2
"Angola" 2006 2597.9635866386 0 2
"Angola" 2007 3121.34872503374 0 2
"Angola" 2008 4081.71750626182 0 2
"Angola" 2009 3123.69888517374 0 2
"Angola" 2010 3586.66369358621 0 2
"Angola" 2011 4608.15516572683 0 2
"Angola" 2012 5083.82687346735 0 2
"Angola" 2013 5061.34925297484 0 2
"Angola" 2014 5011.9844274536 0 2
"Angola" 2015 3217.33924005417 0 2
"Angola" 2016 1809.70937719455 0 2
"Angola" 2017 2439.37443934002 0 2
"Angola" 2018 2540.50887884546 0 2
"Angola" 2019 2191.34776433537 0 2
"Benin" 1980 366.529526562753 0 3
"Benin" 1981 327.565035251454 0 3
"Benin" 1982 312.693212499311 0 3
"Benin" 1983 262.577339668772 0 3
"Benin" 1984 244.844367501528 0 3
"Benin" 1985 236.610586129281 0 3
"Benin" 1986 293.559632058489 0 3
"Benin" 1987 333.22311887927 0 3
"Benin" 1988 335.341923087306 0 3
"Benin" 1989 301.684930793 0 3
"Benin" 1990 381.805056269058 0 3
"Benin" 1991 375.291995673292 0 3
"Benin" 1992 310.623720074924 0 3
"Benin" 1993 398.613027305189 0 3
"Benin" 1994 269.790586928328 0 3
"Benin" 1995 358.822986995425 0 3
"Benin" 1996 380.588286871895 0 3
"Benin" 1997 355.142071829216 0 3
"Benin" 1998 372.877346566327 0 3
"Benin" 1999 541.702259297898 0 3
end
label values numeric_country numeric_country
label def numeric_country 1 "Algeria", modify
label def numeric_country 2 "Angola", modify
label def numeric_country 3 "Benin", modify

[Jared Greathouse]

Listen, I already said I do not know the issue. Show me the output of

xtdescribe

please.

[Tshego Mello]

Thank you for the assistence. I'm sorry if that aggressive or rude

here is the ouput.
. xtdescribe

numeric_country: 1, 2, ..., 51 n = 5
> 1
Years: 1980, 1981, ..., 2019 T = 4
> 0
Delta(Years) = 1 unit
Span(Years) = 40 periods
(numeric_country*Years uniquely identifies each observation)

Distribution of T_i: min 5% 25% 50% 75% 95%
> max
40 40 40 40 40 40
> 40

Freq. Percent Cum. | Pattern
---------------------------+------------------------------------------
51 100.00 100.00 | 1111111111111111111111111111111111111111
---------------------------+------------------------------------------
51 100.00 | XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

[Jared Greathouse]

It's fine, I guess my point is that... synth is such an odd bird sometimes. Its syntax and error codes aren't user friendly, always. In general, a conformability error means that there's a mismatch of matrix dimensions. But, the reason is esoteric in this case. No output is provided (by the synth authors), so it's kind of hard to say why this issue comes up.

The reason I suggested FDID is because it does not suffer from those kinds of issues. It selects a subset of controls, just like synth does, and provides valid Inference (which synth does not do). In other words, if synth does not work for some reason, you can try the next best thing, or the implementation of SCM from sdid or sdid_event.

Plus, I'm the author of fdid, meaning I can better diagnose any issues you have with using it. So, unless you for some reason "must" use SCM, I think FDID is your better buy.