I have a dataset representing a treated unit (GDP), a series of estimated counterfactuals, (cf) and pointwise treatment effects (diff_). Let's inspect what I've got.
I calculate the Root Mean Squared Errors and their ATTs. But, I need each row to reflect the country/unit that was left out. How might I do this? Could I, perhaps extract the string from the variable label? Every string after "Sans", so we'd have "All, Spain..." and so on as the row names? Oh, and as usual, I'm open to any Mata implementation that accomplishes this , as well as any simplification one might offer. Thanks so much!!
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input long gdp double cf int year float(diff_ diff_19) double cf_19 float diff_4 double cf_4 float diff_1 double cf_1 float relative
2284 2382.1523483077835 1960 -98.15235 -239.61543 2523.615430234012 -128.92287 2412.922868887859 -156.5139 2440.5139206906247 -30
2388 2446.6506511192324 1961 -58.65065 -189.2806 2577.2805809334377 -178.53693 2566.53692273034 -142.385 2530.38499218418 -29
2527 2604.8115880199935 1962 -77.81158 -188.0124 2715.0123993333445 -186.70563 2713.7056316563535 -115.07682 2642.0768231463126 -28
2610 2724.349430948411 1963 -114.34943 -210.40553 2820.405534499806 -233.68675 2843.686754138453 -157.42615 2767.4261537616703 -27
2806 2889.9580019727537 1964 -83.958 -178.27428 2984.2742718124796 -115.0309 2921.030893136066 -140.29448 2946.294474600189 -26
3005 3099.1157042307163 1965 -94.11571 -175.01315 3180.013158727885 -36.343998 3041.343998243464 -104.68647 3109.686466545085 -25
3168 3359.960375695906 1966 -191.96037 -266.1019 3434.101900463562 -82.81771 3250.817708028666 -132.79723 3300.7972267528758 -24
3241 3520.2771709761573 1967 -279.27716 -344.3777 3585.3776979131167 -250.44392 3491.4439182434617 -239.84328 3480.8432762798575 -23
3571 3818.1429155125434 1968 -247.1429 -298.81952 3869.8195290516924 -227.67203 3798.6720259284243 -198.46587 3769.4658603651965 -22
3998 4115.411017936789 1969 -117.41102 -151.486 4149.485996602814 -141.20108 4139.20108234847 -103.13166 4101.131661014288 -21
4367 4332.029471182796 1970 34.970528 -7.026773 4374.026773141374 -175.03514 4542.035139627704 -47.44807 4414.448068668981 -20
4686 4657.291964804378 1971 28.708035 -11.247523 4697.247522840271 -105.25514 4791.255143064456 -61.78067 4747.780672611365 -19
5055 5086.462070494702 1972 -31.46207 -53.92624 5108.926233933297 -23.07299 5078.072991847272 -54.10266 5109.102663884323 -18
5553 5646.499101450986 1973 -93.4991 -82.10171 5635.101705764349 -54.6655 5607.665499150371 -142.69818 5695.698185910818 -17
6074 6130.730898105595 1974 -56.7309 -22.579327 6096.579327380564 -227.6143 6301.614301823399 -153.5402 6227.540215496327 -16
6603 6635.678217863695 1975 -32.67822 9.549131 6593.450868564118 -86.40922 6689.409220964208 -160.1954 6763.195415566906 -15
7367 7310.191521860769 1976 56.80848 100.64253 7266.357469068064 -60.40121 7427.401213899765 -8.467811 7375.4678110799205 -14
8090 8024.42535038122 1977 65.574646 84.27509 8005.7249110951425 90.03732 7999.96268731265 49.71434 8040.285657605269 -13
8928 8926.148259121721 1978 1.851741 57.40613 8870.593869525446 153.5217 8774.478301441524 64.27234 8863.727662437957 -12
10067 9836.536517859578 1979 230.4635 223.5902 9843.409790618043 162.4379 9904.562110128869 216.52855 9850.471452859647 -11
11083 10666.661733228293 1980 416.3383 432.9714 10650.02859426265 51.57676 11031.42324635798 401.3458 10681.654217847243 -10
12115 11806.043361600614 1981 308.9566 341.7199 11773.280100739687 63.06381 12051.936191465373 469.9802 11645.019818629977 -9
12761 12235.306759606703 1982 525.69324 539.0721 12221.927891952286 -23.557064 12784.557063637207 143.00787 12617.992126276697 -8
13519 13117.384658666579 1983 401.6154 378.0477 13140.952282833236 157.58456 13361.41543366107 83.95309 13435.046902355898 -7
14481 14362.574193051578 1984 118.4258 156.2245 14324.775496315307 334.3267 14146.67328931739 294.18005 14186.819958095495 -6
15291 15243.436752898206 1985 47.56325 83.27309 15207.726906842216 347.2524 14943.747610653902 325.0587 14965.941300613735 -5
15998 15972.494991886613 1986 25.50501 82.64354 15915.356458415343 397.9014 15600.098567980867 276.6529 15721.347114126615 -4
16679 16859.815540052205 1987 -180.81554 -36.51328 16715.513278612565 228.1159 16450.88409695461 42.18883 16636.811167643467 -3
17786 18036.168024604918 1988 -250.16803 -42.37479 17828.374794129002 157.76624 17628.23376836272 -155.0739 17941.073893747514 -2
18994 19248.291406558565 1989 -254.2914 7.740776 18986.25922439489 193.78767 18800.212319007103 -92.95484 19086.954839201564 -1
20465 20224.150759576136 1990 240.84924 525.81714 19939.182854950133 514.29364 19950.706386596594 393.9321 20071.067943179816 0
21602 20738.400966532055 1991 863.5991 1154.2319 20447.76805299464 685.5661 20916.433899914013 941.9903 20660.009702585576 1
22154 21580.174302957952 1992 573.8257 853.8572 21300.142823316975 676.8211 21477.178907646707 866.7733 21287.226699563194 2
21878 22271.115165831437 1993 -393.1152 -109.473 21987.47300196588 -40.68504 21918.685034424747 8.941148 21869.058852596623 3
22371 23360.161454596157 1994 -989.1614 -676.4664 23047.466398700126 -405.9901 22776.9901324677 -620.2714 22991.271386514 4
23035 24195.70462781342 1995 -1160.7046 -803.5131 23838.513081502944 -767.8742 23802.874198338817 -963.2782 23998.278215525206 5
23742 25245.9743769779 1996 -1503.9744 -1161.2825 24903.282474010928 -816.0538 24558.05377771829 -1319.3065 25061.306472021763 6
24156 26466.868505360024 1997 -2310.8684 -1861.107 26017.10710408016 -1019.7327 25175.73266554645 -2332.7148 26488.71495195701 7
24931 27612.192320207047 1998 -2681.1924 -2129.7876 27060.787551848938 -1351.1836 26282.183542873412 -2691.8235 27622.823511233928 8
25755 28983.168196963157 1999 -3228.168 -2599.467 28354.466950375485 -1130.8975 26885.89751671589 -2711.866 28466.865748668202 9
26943 30458.162643152937 2000 -3515.1626 -2849.585 29792.58513357836 -1400.6198 28343.619691990345 -3381.886 30324.886080181062 10
27449 31282.417577475535 2001 -3833.4175 -2956.631 30405.631330885048 -1783.003 29232.00306631015 -4500.46 31949.46011041869 11
28348 32233.17794185473 2002 -3885.178 -2846.022 31194.021767452297 -1672.4836 30020.483594424702 -5242.954 33590.953480535565 12
28855 33459.1985655981 2003 -4604.1987 -3463.965 32318.965141239547 -1398.5902 30253.59023557029 -5341.003 34196.00272261752 13
end
format %ty year
label var gdp "Real West Germany"
label var cf "Sans All"
label var diff_19 "Effect Sans Spain"
label var cf_19 "Sans Spain"
label var diff_4 "Effect Sans Belgium"
label var cf_4 "Sans Belgium"
label var diff_1 "Effect Sans USA"
label var cf_1 "Sans USA"
label var relative "Relative Time to German Reunification"
cls
// Our Fit
mata A=J(0,1,.)
tempname e123
foreach x of var cf* {
local cf_lab: variable lab `x'
loc outlab "`cf_lab'" // Grabs the label of our outcome variable
tempvar Yco Ytr
matrix rmse_`x'=J(1, 2, .)
mkmat gdp if rel < 0, mat(`Ytr')
mkmat `x' if rel < 0, mat(`Yco')
mat loss = (`Ytr' - `Yco')' * ( `Ytr' - `Yco')
mat loss = loss / rowsof(`Ytr')
mata: roottaker("loss")
mat rowname loss = "`outlab'"
mat colname loss = "RMSPE"
matrix `e123'=loss
mata: A=A\st_matrix("`e123'")
}
mata: st_matrix("B",A)
mata C="RMSE"
mata C=C,J(1,1," ")
mata st_matrix("B",A)
mata st_matrixcolstripe("B",C)
mat l B
// Our ATTS
cls
mata T=J(0,1,.)
tempname e123
foreach x of var diff_* {
tempvar difftr
local cf_lab: variable lab `x'
loc outlab "`cf_lab'" // Grabs the label of our outcome variable
mkmat `x' if rel >= 0, mat(`difftr')
mat U = J(rowsof(`difftr'),1,1)
mat sum = U'*`difftr'
/* create vector of column (variable) means */
mat meanvec = sum/rowsof(`difftr')
mat rowname meanvec = "`outlab'"
mat colname meanvec = "ATT"
matrix `e123'=meanvec
mata: T=T\st_matrix("`e123'")
}
mata: st_matrix("B",T)
mata C="RMSE"
mata C=C,J(1,1," ")
mata st_matrix("B",T)
mata st_matrixcolstripe("B",C)
mat l B
Comment