I am trying to estimate the average treatment effect for each hour of the day(0-23) in the DID framework. I have hourly data before and after an intervention along with treatment and control groups.
I used the following code.
Code:
forvalues i= 0(1)23{
gen HourD_`i'=0
replace HourD_`i'=1 if hour==`i'
gen TREAT_POST_`i'=el_TREAT*HourD_`i'
}
xtset newid new_hour
xtreg daily_el TREAT_POST_* i.hour Mtemp Sun_D Rain_D i.month i.dow , fe cl(house)What I am worried about is one, how can I estimate the ATE for all the 24-hour groups without dropping one of the hours?
Two, the absence of variation in the estimated values even after dropping one of the hours is a bit strange and I feel this might be because of the mistake in my code.
Could you help me out, please?
Thank you!
Code:
Robust
daily_el Coef. Std. Err. t P>t [95% Conf. Interval]
TREAT_POST_0 -.0007645 .0006074 -1.26 0.223 -.0020315 .0005025
TREAT_POST_1 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_2 0 (omitted)
TREAT_POST_3 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_4 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_5 -.0003287 .0005281 -0.62 0.541 -.0014302 .0007728
TREAT_POST_6 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_7 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_8 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_9 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_10 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_11 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_12 -.0002309 .0005395 -0.43 0.673 -.0013562 .0008944
TREAT_POST_13 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_14 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_15 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_16 -.0003202 .0005283 -0.61 0.551 -.0014221 .0007817
TREAT_POST_17 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_18 -.0003239 .0005281 -0.61 0.547 -.0014255 .0007777
TREAT_POST_19 -.0003231 .0005281 -0.61 0.548 -.0014248 .0007785
TREAT_POST_20 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_21 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_22 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
TREAT_POST_23 -.0002874 .0005307 -0.54 0.594 -.0013943 .0008196
hour
1 .000216 .0001524 1.42 0.172 -.0001018 .0005339
2 .0017935 .0002547 7.04 0.000 .0012622 .0023249
3 .000216 .0001524 1.42 0.172 -.0001018 .0005339
4 .000216 .0001524 1.42 0.172 -.0001018 .0005339
5 .0002573 .0001774 1.45 0.162 -.0001126 .0006273
6 .000216 .0001524 1.42 0.172 -.0001018 .0005339
7 .000216 .0001524 1.42 0.172 -.0001018 .0005339
8 .000216 .0001524 1.42 0.172 -.0001018 .0005339
9 .000216 .0001524 1.42 0.172 -.0001018 .0005339
10 .000216 .0001524 1.42 0.172 -.0001018 .0005339
11 .000216 .0001524 1.42 0.172 -.0001018 .0005339
12 .0001595 .0001293 1.23 0.231 -.0001101 .0004292
13 .000216 .0001524 1.42 0.172 -.0001018 .0005339
14 .000216 .0001524 1.42 0.172 -.0001018 .0005339
15 .000216 .0001524 1.42 0.172 -.0001018 .0005339
16 .0002488 .0001728 1.44 0.165 -.0001115 .0006092
17 .000216 .0001524 1.42 0.172 -.0001018 .0005339
18 .0002526 .0001754 1.44 0.165 -.0001133 .0006185
19 .0002518 .0001749 1.44 0.165 -.000113 .0006166
20 .000216 .0001524 1.42 0.172 -.0001018 .0005339
21 .000216 .0001524 1.42 0.172 -.0001018 .0005339
22 .000216 .0001524 1.42 0.172 -.0001018 .0005339
23 .000216 .0001524 1.42 0.172 -.0001018 .0005339
Mtemp -.0114042 .0020218 -5.64 0.000 -.0156216 -.0071868
Sun_D -.0021843 .0002349 -9.30 0.000 -.0026744 -.0016942
Rain_D .0024698 .0018901 1.31 0.206 -.0014729 .0064125
month
2 -.2291243 .0453624 -5.05 0.000 -.3237486 -.1345001
3 -.3958421 .0799676 -4.95 0.000 -.5626516 -.2290325
4 -.4057816 .090535 -4.48 0.000 -.5946343 -.2169289
5 -.3764521 .0780887 -4.82 0.000 -.5393422 -.213562
6 -.6434301 .0756362 -8.51 0.000 -.8012045 -.4856558
7 -.8477705 .0927988 -9.14 0.000 -1.041345 -.6541956
8 -.6645718 .079627 -8.35 0.000 -.8306707 -.4984728
9 -.3498473 .0692479 -5.05 0.000 -.494296 -.2053987
10 -.2765052 .0493688 -5.60 0.000 -.3794868 -.1735237
11 -.0807559 .0361378 -2.23 0.037 -.1561379 -.0053739
12 -.0630829 .0398767 -1.58 0.129 -.1462643 .0200985
dow
1 -.5071416 .0487026 -10.41 0.000 -.6087333 -.4055498
2 -.5541886 .0505661 -10.96 0.000 -.6596678 -.4487095
3 -.5538548 .0503424 -11.00 0.000 -.6588672 -.4488425
4 -.5779501 .0465131 -12.43 0.000 -.6749748 -.4809254
5 -.671445 .0518098 -12.96 0.000 -.7795184 -.5633716
6 -.3100172 .0264164 -11.74 0.000 -.3651208 -.2549135
_cons 5.657422 .0810224 69.83 0.000 5.488412 5.826432
sigma_u 1.9381897
sigma_e 2.3054332
rho .41410322 (fraction of variance due to u_i)Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int year byte(month day hour) float(newid house daily_el POST_treat new_hour Rain_D Sun_D Mtemp) 2015 3 1 22 201 4 . 0 23 0 13.480708 1.54375 2015 3 7 20 136 19 7.21 0 165 4 11.743542 2.3379166 2015 3 18 3 95 4 3.963 0 412 0 37.16121 -.6054167 2015 4 4 13 46 2 .829 0 829 0 86.819 1.2154167 2015 4 15 20 177 17 8.139 0 1100 .4 99.15254 2.1029167 2015 5 5 7 272 5 2.877 0 1567 1 58.138 6.392917 2015 5 22 9 193 16 2.698 0 1977 .8 189.77013 8.161667 2015 5 26 10 28 3 1.745 0 2074 .8 72.12363 8.792084 2015 6 9 14 135 4 3.298 0 2414 0 245.30713 11.045417 2015 6 11 15 265 6 5.141 0 2463 4.2 280.0603 12.217083 2015 7 2 21 66 3 3.319 0 2973 0 257.1807 19.2325 2015 7 3 22 211 20 7.211 0 2998 0 267.18765 21.339167 2015 7 6 3 328 9 6.104 0 3051 1 229.7934 13.185833 2015 7 18 4 422 15 4.84 0 3340 4.2 138.00366 14.010834 2015 7 18 1 334 9 1.368 0 3337 4.2 138.00366 14.010834 2015 7 20 14 79 17 2.805 0 3398 0 287.48047 16.010834 2015 7 23 3 264 7 2.384 0 3459 0 190.2473 15.1175 2015 8 2 13 177 17 9.157 0 3709 1 201.73784 15.447083 2015 8 12 2 186 17 21.59 0 3938 0 166.55537 16.942917 2015 9 5 9 106 18 5.208 0 4521 5.4 101.7728 12.477083 2015 9 15 16 125 18 2.215 0 4768 2.8 88.38121 13.040833 2015 9 16 21 11 21 3.843 0 4797 7 43.83754 13.78875 2015 9 22 11 350 9 2.055 0 4931 1.8 46.84613 11.625 2015 10 11 11 297 5 1.783 0 5387 .2 68.78709 5.565834 2015 10 21 10 286 7 1.229 0 5626 3.8 17.485125 6.346667 2015 10 26 21 154 19 3.798 0 5757 0 38.62704 4.701667 2015 11 14 0 338 9 11.23 0 6192 4.2 9.128375 2.2108333 2015 11 21 16 510 12 1.113 0 6376 0 1.9329584 -3.195 2015 12 4 1 390 11 5.942 0 6673 1.8 .6641667 .22416666 2015 12 17 20 101 21 2.608 0 7004 2.6 1.1192083 -1.13625 2016 1 12 17 73 21 6.967 0 7625 0 3.602625 -7.483333 2016 1 13 22 339 9 7.811 0 7654 0 4.0272083 -13.66625 2016 1 13 6 173 21 8.465 0 7638 0 4.0272083 -13.66625 2016 1 21 16 467 10 4.244 0 7840 0 11.917417 -21.87917 2016 1 26 17 309 6 8.951 0 7961 3.6 4.79125 .29125 2016 1 30 18 483 13 9.761 0 8058 3.8 14.1975 1.9404167 2016 2 2 11 5 3 3.107 0 8123 0 13.33775 -2.3733332 2016 2 4 19 131 19 5.556 0 8179 0 21.470667 -6.76125 2016 2 5 8 330 8 3.74 0 8192 0 26.51871 -8.507083 2016 2 8 3 244 3 3.214 0 8259 12 9.762792 2.0208333 2016 2 10 15 332 5 1.144 0 8319 3.8 7.794209 .96 2016 2 10 16 252 3 6.488 0 8320 3.8 7.794209 .96 2016 2 16 8 345 8 4.294 0 8456 .4 41.71217 -5.479167 2016 2 20 3 323 8 2.841 0 8547 0 10.438167 -1.4541667 2016 3 16 14 280 5 6.648 1 9158 0 47.11192 6.822083 2016 3 22 1 334 9 2.151 1 9289 0 82.75638 -6.194167 2016 4 15 19 24 21 1.634 1 9882 0 47.98571 .07125 2016 4 24 5 396 11 4.902 1 10084 0 182.00195 1.5391667 2016 5 3 14 423 14 4.747 1 10309 0 226.62534 8.055 2016 5 15 8 452 15 4.849 1 10591 0 225.0284 9.1325 2016 5 19 0 10 21 2.069 1 10679 3.6 61.68958 6.175416 2016 5 27 11 156 1 2.658 1 10882 0 282.04114 13.697917 2016 6 5 8 174 17 4.945 1 11095 0 260.2243 9.830417 2016 6 7 16 341 8 3.403 1 11151 0 223.6433 14.974584 2016 6 14 5 286 7 4.869 1 11308 0 259.2458 13.575417 2016 6 18 10 221 20 10.131 1 11409 12.8 54.11317 13.714583 2016 6 19 22 494 13 3.822 1 11445 0 89.41571 14.87 2016 6 24 15 281 6 8.607 1 11558 0 191.99384 15.170417 2016 7 2 13 227 21 1.714 1 11748 13 53.05762 15.698334 2016 7 5 0 17 21 3.771 1 11807 0 173.136 17.132084 2016 7 7 2 296 5 8.209 1 11857 .2 159.87416 15.515417 2016 7 13 3 485 10 3.187 1 12002 0 266.129 19.114166 2016 7 19 19 65 2 4.738 1 12162 0 300.87234 16.009583 2016 7 28 3 525 13 6.826 1 12362 0 195.97725 18.712084 2016 7 30 6 81 4 2.0640001 1 12413 0 226.72183 18.664167 2016 8 24 9 456 14 2.591 1 13016 0 167.04108 13.781667 2016 8 24 16 269 6 5.687 1 13023 0 167.04108 13.781667 2016 9 5 20 192 3 2.734 1 13315 .2 167.18237 11.800417 2016 9 16 9 406 8 3.388 1 13568 0 131.39209 8.85 2016 9 22 23 58 1 2.545 1 13726 0 61.80125 11.405833 2016 10 4 9 483 13 8.934 1 14000 0 85.08234 3.82125 2016 10 17 20 96 2 5.345 1 14323 9.2 8.0875 6.18 2016 10 23 5 268 6 3.39 1 14452 0 6.313375 3.574583 2016 10 31 13 260 11 4.188 1 14652 0 29.49279 -.13416667 2016 11 5 6 328 9 4.625 1 14765 0 13.093542 -6.461667 2016 11 6 8 276 5 1.597 1 14791 0 12.343667 -6.18875 2016 11 8 7 471 13 10.854 1 14838 0 16.364584 -3.54625 2016 11 19 8 334 9 1.423 1 15103 6.4 4.775583 2.990417 2016 11 21 16 11 21 3.842 1 15159 10.6 1.3366667 3.91875 2016 11 25 4 219 20 3.435 1 15243 13 2.0737917 -3.162917 2016 11 27 9 125 18 3.548 1 15296 0 5.512542 -3.375833 2016 12 4 10 76 4 8.716 1 15465 3.2 1.141625 -5.406667 2016 12 5 22 77 17 2.712 1 15501 3 3.381917 .7016667 2016 12 5 21 507 12 5.606 1 15500 3 3.381917 .7016667 2016 12 6 8 233 21 4.868 1 15511 0 3.407917 -8.067083 2016 12 20 8 380 11 4.701 1 15847 .2 2.6482084 3.185833 2016 12 28 23 270 7 12.021 1 16054 0 3.18725 -1.0516666 2017 1 4 1 77 17 3.644 1 16200 0 2.210875 -12.480833 2017 1 7 13 394 15 1.809 1 16284 0 2.645708 -2.2695832 2017 1 9 13 273 5 4.376 1 16332 1.4 2.523833 .55875 2017 1 11 1 459 10 5.846 1 16368 9.8 .53554165 1.0320834 2017 1 20 16 192 3 7.992 1 16599 0 6.308167 -2.0870833 2017 1 23 18 474 10 6.009 1 16673 2 6.978875 2.5258334 2017 1 24 5 256 21 3.26 1 16684 0 7.893291 -5.292083 2017 1 24 0 84 4 11.7 1 16679 0 7.893291 -5.292083 2017 1 31 11 219 20 .583 1 16858 0 10.181958 -3.621667 2017 2 7 15 24 21 6.028 1 17030 0 22.425083 -8.836667 2017 2 25 0 413 8 15.744 1 17447 .2 58.82933 -4.7108335 2017 2 26 3 147 1 1.32 1 17474 0 60.76804 -8.837083 2017 2 28 8 134 19 2.791 1 17527 0 11.151958 -1.0383333 end
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