Linked to a query I asked yesterday, I decided to forego the meta-analysis route and use the simple average of treatment effects. When I try and run my new code, I only get one coefficient plotted. Here's my dataset and code. Note that I use the user written distinct, coefplot, and tabstatmat, which I include the code to install them should you not have them.
And while it looks nice, I want to present the rest of the matrix Z, too. How might I solve this?
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input int Year long id float(diff_ te_lb_ te_ub_) double cf_ float relative_ str17 id2
2003 13 -3368.3206 -4512.5894 -2224.0518 5182.125571428572 -7 "Berlin-Sch"
2004 13 -1746.3706 -2890.6394 -602.1019 5182.125571428572 -6 "Berlin-Sch"
2005 13 -61.56657 -1205.8353 1082.7021 5182.125571428572 -5 "Berlin-Sch"
2006 13 909.8405 -234.42825 2054.1091 5182.125571428572 -4 "Berlin-Sch"
2007 13 1166.1074 21.83872 2310.3762 5182.125571428572 -3 "Berlin-Sch"
2008 13 1469.0585 324.7898 2613.327 5182.125571428572 -2 "Berlin-Sch"
2009 13 1631.2515 486.9828 2775.52 5182.125571428572 -1 "Berlin-Sch"
2010 13 2139.8635 995.5948 3284.132 5182.125571428572 0 "Berlin-Sch"
2011 13 1945.0624 800.7937 3089.331 5182.125571428572 1 "Berlin-Sch"
2012 13 1929.8124 785.5437 3074.081 5182.125571428572 2 "Berlin-Sch"
2013 13 1559.2465 414.97775 2703.515 5182.125571428572 3 "Berlin-Sch"
2014 13 2115.3125 971.0438 3259.581 5182.125571428572 4 "Berlin-Sch"
2015 13 3348.3635 2204.0947 4492.6323 5182.125571428572 5 "Berlin-Sch"
2003 14 -17.596174 -2291.8364 2256.644 11167.7911747892 -7 "Berlin-Teg"
2004 14 -71.779205 -2346.0193 2202.461 11155.499202925053 -6 "Berlin-Teg"
2005 14 -12.326933 -2286.5671 2261.9133 11578.18493246791 -5 "Berlin-Teg"
2006 14 -22.442225 -2296.6824 2251.7979 11859.192225124858 -4 "Berlin-Teg"
2007 14 42.16394 -2232.0762 2316.404 13325.768059737678 -3 "Berlin-Teg"
2008 14 54.4165 -2219.8237 2328.6567 14445.473496454233 -2 "Berlin-Teg"
2009 14 27.56409 -2246.676 2301.8042 14172.973908501066 -1 "Berlin-Teg"
2010 14 462.8938 -1811.3464 2737.134 14594.594199177489 0 "Berlin-Teg"
2011 14 2222.3154 -51.92476 4496.5557 14712.518640579834 1 "Berlin-Teg"
2012 14 3389.439 1115.199 5663.679 14780.198704235157 2 "Berlin-Teg"
2013 14 4477.0166 2202.7764 6751.257 15118.534518469274 3 "Berlin-Teg"
2014 14 5471.214 3196.974 7745.455 15223.320600748859 4 "Berlin-Teg"
2015 14 6099.445 3825.2046 8373.685 14903.533373143386 5 "Berlin-Teg"
2003 20 -2.3517873 -228.185 223.4814 1602.9607872116499 -7 "Bremen"
2004 20 -11.124884 -236.95807 214.7083 1648.3888840234827 -6 "Bremen"
2005 20 2.026804 -223.8064 227.86 1707.9801960079526 -5 "Bremen"
2006 20 -20.499905 -246.3331 205.3333 1698.1239037273085 -4 "Bremen"
2007 20 13.111323 -212.72186 238.9445 2206.3736770960195 -3 "Bremen"
2008 20 5.189031 -220.64417 231.0222 2471.519969283851 -2 "Bremen"
2009 20 13.649417 -212.18378 239.4826 2421.4725826497356 -1 "Bremen"
2010 20 -77.12698 -302.96017 148.7062 2734.4119868367616 0 "Bremen"
2011 20 -273.4868 -499.32 -47.65359 2826.118778758761 1 "Bremen"
2012 20 -499.3661 -725.1993 -273.53287 2941.3030591834136 2 "Bremen"
2013 20 -568.0962 -793.9294 -342.263 3173.690169805002 3 "Bremen"
2014 20 -806.099 -1031.9323 -580.2658 3573.827007438604 4 "Bremen"
2015 20 -3.744276 -229.57747 222.0889 2661.3572758678038 5 "Bremen"
2003 26 -55.58367 -1095.3663 984.199 2078.8736662910887 -7 "Charleroi"
2004 26 -28.888126 -1068.6708 1010.8945 2052.1781265967475 -6 "Charleroi"
2005 26 -96.20045 -1135.983 943.5822 1959.4154523721095 -5 "Charleroi"
2006 26 -56.76433 -1096.547 983.0183 2211.3473308617517 -4 "Charleroi"
2007 26 37.098186 -1002.6844 1076.8809 2406.085812333945 -3 "Charleroi"
2008 26 55.20294 -984.5797 1094.9856 2889.122062891687 -2 "Charleroi"
2009 26 145.13545 -894.6472 1184.9181 3775.032548652671 -1 "Charleroi"
2010 26 1552.1245 512.34186 2591.907 3628.582536103094 0 "Charleroi"
2011 26 2137.1313 1097.3488 3176.914 3746.6797013076707 1 "Charleroi"
2012 26 2353.0037 1313.221 3392.7864 4154.4533261728575 2 "Charleroi"
2013 26 1965.8728 926.0901 3005.6555 4812.593172521729 3 "Charleroi"
2014 26 1467.222 427.4395 2507.005 4962.462777682557 4 "Charleroi"
2015 26 1979.4617 939.679 3019.2444 4967.905307814248 5 "Charleroi"
2003 29 -38.91276 -149.88506 72.05954 1062.2417593199111 -7 "Dortmund"
2004 29 -59.6762 -170.6485 51.2961 1196.9691957924997 -6 "Dortmund"
2005 29 3.620633 -107.35167 114.59293 1700.3963670135172 -5 "Dortmund"
2006 29 25.32081 -85.65149 136.2931 1955.3471906556097 -4 "Dortmund"
2007 29 16.784462 -94.18784 127.75676 2096.243538878761 -3 "Dortmund"
2008 29 55.13542 -55.83688 166.1077 2252.30458029289 -2 "Dortmund"
2009 29 -2.272368 -113.24467 108.69994 1709.740368046811 -1 "Dortmund"
2010 29 -85.78138 -196.7537 25.19092 1836.412377506224 0 "Dortmund"
2011 29 -230.2844 -341.2567 -119.3121 2055.8643870445467 1 "Dortmund"
2012 29 -87.45404 -198.42635 23.51826 1991.128041960525 2 "Dortmund"
2013 29 -115.5751 -226.5474 -4.602796 2038.2660966134827 3 "Dortmund"
2014 29 -310.1897 -421.162 -199.21736 2267.998662034795 4 "Dortmund"
2015 29 -313.3759 -424.3481 -202.40355 2288.0858410440537 5 "Dortmund"
2003 30 -24.08678 -24.534933 -23.63863 1623.6517831290585 -7 "Dresden"
2004 30 -19.88877 -20.33692 -19.440617 1684.2427673176328 -6 "Dresden"
2005 30 6.369642 5.921491 6.817793 1813.6443579518448 -5 "Dresden"
2006 30 8.377583 7.929431 8.825734 1866.6124175526952 -4 "Dresden"
2007 30 16.81497 16.36682 17.26312 1871.2760302006793 -3 "Dresden"
2008 30 17.365341 16.91719 17.813492 1871.4126582070253 -2 "Dresden"
2009 30 -4.951986 -5.400137 -4.5038347 1755.4549856410654 -1 "Dresden"
2010 30 127.6259 127.17775 128.07405 1753.7391030772287 0 "Dresden"
2011 30 130.40588 129.95773 130.85403 1801.440118010775 1 "Dresden"
2012 30 106.0393 105.59114 106.48744 1794.6027097656156 2 "Dresden"
2013 30 -46.68348 -47.13163 -46.23532 1809.718476786496 3 "Dresden"
2014 30 -70.30171 -70.74986 -69.85356 1839.7567113986884 4 "Dresden"
2015 30 -115.19188 -115.64003 -114.74373 1841.367876212788 5 "Dresden"
2003 31 -67.97996 -589.7357 453.7758 14193.423958572419 -7 "Dusseldorf"
2004 31 -13.094735 -534.85046 508.661 15106.496735588189 -6 "Dusseldorf"
2005 31 -52.85564 -574.6114 468.9001 15445.557645145764 -5 "Dusseldorf"
2006 31 .52006716 -521.23566 522.2758 16510.372932836595 -4 "Dusseldorf"
2007 31 34.104366 -487.6514 555.8601 17748.068633450242 -3 "Dusseldorf"
2008 31 63.89149 -457.8643 585.6472 18040.496512975365 -2 "Dusseldorf"
2009 31 35.414417 -486.3413 557.17017 17690.72658143142 -1 "Dusseldorf"
2010 31 731.5831 209.8274 1253.339 18178.11988091303 0 "Dusseldorf"
2011 31 1633.2177 1111.4619 2154.9734 18665.752398752622 1 "Dusseldorf"
2012 31 1604.842 1083.0864 2126.598 19195.221877165117 2 "Dusseldorf"
2013 31 1605.985 1084.2294 2127.741 19590.37593700064 3 "Dusseldorf"
2014 31 463.9277 -57.82802 985.6835 21353.032277202972 4 "Dusseldorf"
2015 31 -338.8702 -860.626 182.8855 22787.085252026827 5 "Dusseldorf"
2003 35 -50.87827 -1300.0736 1198.317 2385.6392729253243 -7 "Frankfurt"
2004 35 -35.44301 -1284.6383 1213.7523 2776.015006711667 -6 "Frankfurt"
2005 35 -32.307198 -1281.5024 1216.8881 3032.723197229319 -5 "Frankfurt"
2006 35 18.691252 -1230.504 1267.8865 3672.221748043358 -4 "Frankfurt"
2007 35 44.18363 -1205.0116 1293.379 4028.3373713769947 -3 "Frankfurt"
2008 35 23.926146 -1225.269 1273.1215 3888.921855341137 -2 "Frankfurt"
2009 35 31.82745 -1217.3678 1281.0227 3719.6495483722006 -1 "Frankfurt"
2010 35 -456.0636 -1705.259 793.1317 3930.0196112707877 0 "Frankfurt"
2011 35 -1864.72 -3113.915 -615.5246 4810.318869873561 1 "Frankfurt"
end
format %ty Year
label values id Airport
label def Airport 13 "Berlin-Sch", modify
label def Airport 14 "Berlin-Teg", modify
label def Airport 20 "Bremen", modify
label def Airport 26 "Charleroi", modify
label def Airport 29 "Dortmund", modify
label def Airport 30 "Dresden", modify
label def Airport 31 "Dusseldorf", modify
label def Airport 35 "Frankfurt", modify
foreach v in tabstatmat distinct coefplot {
cap which `v'
if _rc {
ssc inst `v', replace
}
}
qui tabstat diff_ te_lb_ te_ub_ if rel >= 0, stat(mean) by(id2) save
tabstatmat Z
mat colnames Z = ES LB UB
qui levelsof id2, l(unit_names)
mat rownames Z = `unit_names' Overall
mat l Z
qui distinct id
loc last =r(ndistinct)+1
loc ATT: di %6.4g Z[`last',1]
cls
di `ATT'
coefplot matrix(Z[,1]), ci((2 3)) xline(1)
Comment