Consider the following in ado-code
This estimates the causal impact of West German Reunification on GDP per capita, using a new difference-in-differences technique. The steps are fairly simple: Take the row-wise average of the control group's units, then use bivariate linear regression to predict the outcome of the treated unit (the first column) in the preintervention period (pre-1990). Then, use those OLS estimates from the pre-intervention period, and boom, we have ourselves a counterfactual.
Consider the problem now in Mata
The error you'll get is
'if' found where almost anything else expected
r(3000);
My question is this: how do I get Mata to know that I'd like to restrict my estimation sample to being before 1990, and then use the OLS coefficient to predict the values for West Germany after 1990? In Stata's ado, this is very simple. But I've never used Mata before, so I was curious on how we might do this.
EDIT: For the curious, this is code for this paper and the Mata code comes from here
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input int year long(gdp7 gdp1 gdp2 gdp3 gdp4 gdp5 gdp6 gdp8 gdp9 gdp10 gdp12 gdp14 gdp16 gdp18 gdp19 gdp20 gdp21)
1960 2284 2879 2158 1796 1782 2329 1858 1620 2113 1713 3387 1010 707 728 1074 2373 2545
1961 2388 2929 2216 1899 1883 2485 1959 1763 2111 1823 3625 1143 1028 783 1201 2346 2659
1962 2527 3103 2276 1977 2018 2665 2099 1900 2275 1905 3788 1262 1049 848 1335 2539 2713
1963 2610 3227 2386 2074 2122 2700 2205 2021 2360 1991 3946 1374 1188 905 1467 2717 2866
1964 2806 3420 2534 2224 2288 2980 2367 2093 2566 2112 4158 1546 1316 979 1554 2873 3001
1965 3005 3667 2660 2336 2414 3177 2526 2205 2739 2268 4360 1647 1492 1084 1678 2973 3224
1966 3168 3974 2794 2537 2559 3377 2728 2400 2874 2417 4575 1889 1634 1170 1841 3230 3481
1967 3241 4154 2923 2668 2719 3580 2915 2624 3078 2620 4790 2133 1754 1293 1954 3404 3360
1968 3571 4494 3173 2908 2958 3892 3163 2910 3399 2784 5138 2496 1967 1475 2160 3788 3489
1969 3998 4805 3391 3239 3308 4352 3531 3227 3951 3035 5645 2917 2298 1592 2447 4162 3969
1970 4367 4999 3615 3732 3797 4541 3857 3602 4342 3213 6382 3293 2631 1916 2715 4490 4136
1971 4686 5362 3854 4100 4129 4858 4203 3834 4705 3523 6938 3575 2988 2153 2951 4677 4438
1972 5055 5838 4149 4513 4512 5262 4535 4101 4984 3814 7406 3988 3383 2420 3293 5013 4780
1973 5553 6464 4683 4969 5039 5719 5008 4594 5475 4138 7998 4491 3844 2845 3710 5595 5335
1974 6074 6951 5036 5622 5707 6121 5594 5240 6165 4728 8800 4778 3907 3048 4227 6024 6041
1975 6603 7519 5481 6147 6146 6565 6077 5601 6699 5458 8934 5300 4505 3045 4600 6683 6382
1976 7367 8300 5953 6810 6854 7371 6673 6288 7345 6116 9368 5768 5024 3407 4965 7140 6748
1977 8090 9146 6489 7588 7326 7901 7292 6821 7959 6742 10240 6344 5417 3784 5364 7482 6867
1978 8928 10229 7176 8117 8056 8585 8031 7542 8676 7412 11003 7084 6138 4120 5761 8272 7301
1979 10067 11306 7973 9288 8921 9566 8948 8594 9504 8354 12189 8027 6781 4663 6188 9174 7915
1980 11083 12186 8502 10312 10156 10362 9891 9643 10458 9557 13853 8931 7374 5263 6856 10203 8656
1981 12115 13533 9161 11242 11079 11106 10929 10593 11304 10548 15338 9986 7870 5812 7447 11513 9736
1982 12761 13940 9917 12148 11827 12115 11869 11275 11784 11205 15963 10813 8204 6263 7957 11537 10687
1983 13519 15008 10669 13048 12334 12822 12518 11812 12419 12022 16611 11346 8388 6479 8378 12300 11262
1984 14481 16549 11336 13533 13113 13777 13145 12543 13234 13220 17710 12064 8834 6570 8812 13120 12141
1985 15291 17600 12068 14296 13735 14698 13746 13285 13938 14354 18812 12978 9297 6959 9259 14019 12556
1986 15998 18439 12795 14921 14292 15608 14308 13896 14613 15184 19458 13590 9525 7414 9744 14537 13085
1987 16679 19407 13717 15549 15013 16024 14940 14688 15197 15823 20120 14425 9548 8126 10542 15554 13402
1988 17786 20711 14864 16595 16209 16766 16040 15784 16082 16299 21334 15862 10277 9057 11434 16524 13569
1989 18994 22047 15716 17768 17345 17418 17193 16875 17387 17043 22965 17269 11036 10042 12417 17255 14124
1990 20465 23064 16397 19070 18526 18237 18244 17946 18665 18004 24518 18815 11405 10894 13365 17322 14420
1991 21602 23507 16681 20172 19453 19070 19021 18845 19626 19212 24840 20055 11971 11783 14152 17652 14252
1992 22154 24509 17069 20960 20123 19829 19746 19367 20224 20185 25141 20648 12198 12219 14564 18496 14679
1993 21878 25409 17845 21220 20308 20199 19920 19778 20679 21088 25427 21122 12166 12236 14699 19432 15605
1994 22371 26670 18975 22139 21340 21691 20695 20577 21588 22534 26031 21757 12566 12614 15325 20583 16686
1995 23035 27574 19860 22976 22248 22693 21545 21532 22585 23874 26485 22551 12991 13413 16032 21773 17402
1996 23742 28814 20923 24008 22687 23781 22288 22235 23531 26263 26394 23714 13418 13974 16720 22562 17879
1997 24156 30262 22280 24472 23416 24878 23370 22810 24692 27776 27850 24478 14127 14804 17420 23649 18510
1998 24931 31519 23206 25314 24164 25669 24412 23840 25811 27294 28835 24429 14665 15401 18479 24853 18669
1999 25755 33028 23959 26558 24792 27113 25111 24402 26654 30011 28887 24709 15213 16363 19817 26283 19937
2000 26943 34603 25583 28359 26631 28798 26690 25759 28467 36273 30461 26015 16272 17353 21074 27403 20789
2001 27449 35341 27026 28855 28001 29837 28043 26586 30359 37078 30806 26619 17332 18071 22257 28492 21825
2002 28348 36180 28969 29942 29330 30318 28829 27320 31284 36617 32751 27196 19119 18799 23756 29819 22662
2003 28855 37548 29609 30796 30082 30853 29210 27537 31792 37245 33516 28071 20479 17603 24812 31273 23728
end
format %ty year
label var gdp7 "West Germany"
label var gdp1 "1 GDP per Capita"
label var gdp2 "2 GDP per Capita"
label var gdp3 "3 GDP per Capita"
label var gdp4 "4 GDP per Capita"
label var gdp5 "5 GDP per Capita"
label var gdp6 "6 GDP per Capita"
label var gdp8 "8 GDP per Capita"
label var gdp9 "9 GDP per Capita"
label var gdp10 "10 GDP per Capita"
label var gdp12 "12 GDP per Capita"
label var gdp14 "14 GDP per Capita"
label var gdp16 "16 GDP per Capita"
label var gdp18 "18 GDP per Capita"
label var gdp19 "19 GDP per Capita"
label var gdp20 "20 GDP per Capita"
label var gdp21 "21 GDP per Capita"
cls
qui ds
loc int_time= 1987
loc temp: word 1 of `r(varlist)'
loc time: disp "`temp'"
loc t: word 2 of `r(varlist)'
loc treated_unit: disp "`t'"
loc a: word 3 of `r(varlist)'
loc donor_one: disp "`a'" // First donor unit...
local nwords : word count `r(varlist)'
loc b: word `nwords' of `r(varlist)'
loc last_donor: disp "`b'" // Last donor...
egen ym = rowmean(`donor_one'-`last_donor')
lab var ym "ymean"
drop `donor_one'-`last_donor'
cls
reg gdp7 ym ///
if year < `int_time' //
predict cf, xb
lab var cf "Counterfactual West Germany"
cls
line gdp cf year, xline(`int_time') ///
legend(ring(0) pos(11)) lcol(black red) ///
yti("GDP per Capita") xti("Year")
Consider the problem now in Mata
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input int year long(gdp7 gdp1 gdp2 gdp3 gdp4 gdp5 gdp6 gdp8 gdp9 gdp10 gdp12 gdp14 gdp16 gdp18 gdp19 gdp20 gdp21)
1960 2284 2879 2158 1796 1782 2329 1858 1620 2113 1713 3387 1010 707 728 1074 2373 2545
1961 2388 2929 2216 1899 1883 2485 1959 1763 2111 1823 3625 1143 1028 783 1201 2346 2659
1962 2527 3103 2276 1977 2018 2665 2099 1900 2275 1905 3788 1262 1049 848 1335 2539 2713
1963 2610 3227 2386 2074 2122 2700 2205 2021 2360 1991 3946 1374 1188 905 1467 2717 2866
1964 2806 3420 2534 2224 2288 2980 2367 2093 2566 2112 4158 1546 1316 979 1554 2873 3001
1965 3005 3667 2660 2336 2414 3177 2526 2205 2739 2268 4360 1647 1492 1084 1678 2973 3224
1966 3168 3974 2794 2537 2559 3377 2728 2400 2874 2417 4575 1889 1634 1170 1841 3230 3481
1967 3241 4154 2923 2668 2719 3580 2915 2624 3078 2620 4790 2133 1754 1293 1954 3404 3360
1968 3571 4494 3173 2908 2958 3892 3163 2910 3399 2784 5138 2496 1967 1475 2160 3788 3489
1969 3998 4805 3391 3239 3308 4352 3531 3227 3951 3035 5645 2917 2298 1592 2447 4162 3969
1970 4367 4999 3615 3732 3797 4541 3857 3602 4342 3213 6382 3293 2631 1916 2715 4490 4136
1971 4686 5362 3854 4100 4129 4858 4203 3834 4705 3523 6938 3575 2988 2153 2951 4677 4438
1972 5055 5838 4149 4513 4512 5262 4535 4101 4984 3814 7406 3988 3383 2420 3293 5013 4780
1973 5553 6464 4683 4969 5039 5719 5008 4594 5475 4138 7998 4491 3844 2845 3710 5595 5335
1974 6074 6951 5036 5622 5707 6121 5594 5240 6165 4728 8800 4778 3907 3048 4227 6024 6041
1975 6603 7519 5481 6147 6146 6565 6077 5601 6699 5458 8934 5300 4505 3045 4600 6683 6382
1976 7367 8300 5953 6810 6854 7371 6673 6288 7345 6116 9368 5768 5024 3407 4965 7140 6748
1977 8090 9146 6489 7588 7326 7901 7292 6821 7959 6742 10240 6344 5417 3784 5364 7482 6867
1978 8928 10229 7176 8117 8056 8585 8031 7542 8676 7412 11003 7084 6138 4120 5761 8272 7301
1979 10067 11306 7973 9288 8921 9566 8948 8594 9504 8354 12189 8027 6781 4663 6188 9174 7915
1980 11083 12186 8502 10312 10156 10362 9891 9643 10458 9557 13853 8931 7374 5263 6856 10203 8656
1981 12115 13533 9161 11242 11079 11106 10929 10593 11304 10548 15338 9986 7870 5812 7447 11513 9736
1982 12761 13940 9917 12148 11827 12115 11869 11275 11784 11205 15963 10813 8204 6263 7957 11537 10687
1983 13519 15008 10669 13048 12334 12822 12518 11812 12419 12022 16611 11346 8388 6479 8378 12300 11262
1984 14481 16549 11336 13533 13113 13777 13145 12543 13234 13220 17710 12064 8834 6570 8812 13120 12141
1985 15291 17600 12068 14296 13735 14698 13746 13285 13938 14354 18812 12978 9297 6959 9259 14019 12556
1986 15998 18439 12795 14921 14292 15608 14308 13896 14613 15184 19458 13590 9525 7414 9744 14537 13085
1987 16679 19407 13717 15549 15013 16024 14940 14688 15197 15823 20120 14425 9548 8126 10542 15554 13402
1988 17786 20711 14864 16595 16209 16766 16040 15784 16082 16299 21334 15862 10277 9057 11434 16524 13569
1989 18994 22047 15716 17768 17345 17418 17193 16875 17387 17043 22965 17269 11036 10042 12417 17255 14124
1990 20465 23064 16397 19070 18526 18237 18244 17946 18665 18004 24518 18815 11405 10894 13365 17322 14420
1991 21602 23507 16681 20172 19453 19070 19021 18845 19626 19212 24840 20055 11971 11783 14152 17652 14252
1992 22154 24509 17069 20960 20123 19829 19746 19367 20224 20185 25141 20648 12198 12219 14564 18496 14679
1993 21878 25409 17845 21220 20308 20199 19920 19778 20679 21088 25427 21122 12166 12236 14699 19432 15605
1994 22371 26670 18975 22139 21340 21691 20695 20577 21588 22534 26031 21757 12566 12614 15325 20583 16686
1995 23035 27574 19860 22976 22248 22693 21545 21532 22585 23874 26485 22551 12991 13413 16032 21773 17402
1996 23742 28814 20923 24008 22687 23781 22288 22235 23531 26263 26394 23714 13418 13974 16720 22562 17879
1997 24156 30262 22280 24472 23416 24878 23370 22810 24692 27776 27850 24478 14127 14804 17420 23649 18510
1998 24931 31519 23206 25314 24164 25669 24412 23840 25811 27294 28835 24429 14665 15401 18479 24853 18669
1999 25755 33028 23959 26558 24792 27113 25111 24402 26654 30011 28887 24709 15213 16363 19817 26283 19937
2000 26943 34603 25583 28359 26631 28798 26690 25759 28467 36273 30461 26015 16272 17353 21074 27403 20789
2001 27449 35341 27026 28855 28001 29837 28043 26586 30359 37078 30806 26619 17332 18071 22257 28492 21825
2002 28348 36180 28969 29942 29330 30318 28829 27320 31284 36617 32751 27196 19119 18799 23756 29819 22662
2003 28855 37548 29609 30796 30082 30853 29210 27537 31792 37245 33516 28071 20479 17603 24812 31273 23728
end
format %ty year
label var gdp7 "West Germany"
label var gdp1 "1 GDP per Capita"
label var gdp2 "2 GDP per Capita"
label var gdp3 "3 GDP per Capita"
label var gdp4 "4 GDP per Capita"
label var gdp5 "5 GDP per Capita"
label var gdp6 "6 GDP per Capita"
label var gdp8 "8 GDP per Capita"
label var gdp9 "9 GDP per Capita"
label var gdp10 "10 GDP per Capita"
label var gdp12 "12 GDP per Capita"
label var gdp14 "14 GDP per Capita"
label var gdp16 "16 GDP per Capita"
label var gdp18 "18 GDP per Capita"
label var gdp19 "19 GDP per Capita"
label var gdp20 "20 GDP per Capita"
label var gdp21 "21 GDP per Capita"
cls
qui ds
loc int_time= 1987
loc temp: word 1 of `r(varlist)'
loc time: disp "`temp'"
loc t: word 2 of `r(varlist)'
loc treated_unit: disp "`t'"
loc a: word 3 of `r(varlist)'
loc donor_one: disp "`a'" // First donor unit...
local nwords : word count `r(varlist)'
loc b: word `nwords' of `r(varlist)'
loc last_donor: disp "`b'" // Last donor...
/*
egen ym = rowmean(`donor_one'-`last_donor')
lab var ym "ymean"
drop `donor_one'-`last_donor'
cls
reg gdp7 ym ///
if year < `int_time' // */
mata
y = st_data(., "gdp7")
t = st_data(., "year")
X = st_data(., "`donor_one'-`last_donor'")
n = rows(X)
X = X,J(n,1,1)
XpX = quadcross(X, X) if t < 1990
XpXi = invsym(XpX)
b = XpXi*quadcross(X, y)
end
'if' found where almost anything else expected
r(3000);
My question is this: how do I get Mata to know that I'd like to restrict my estimation sample to being before 1990, and then use the OLS coefficient to predict the values for West Germany after 1990? In Stata's ado, this is very simple. But I've never used Mata before, so I was curious on how we might do this.
EDIT: For the curious, this is code for this paper and the Mata code comes from here
Comment