stackedev
Table of contents
Notes
- Based on: Cengiz, Dube, Lindner, Zipperer 2019. The effect of minimum wages on low-wage jobs
- Program version (if available): -
- Last checked: 7 Jul 2026
The dofile defines untreated units with no_treat, so the estimation call below uses never_treat(no_treat).
Installation and options
Install the command from SSC:
ssc install stackedev, replace
Take a look at the help file:
help stackedev
Test the command
Define the reference year:
ren F_1 ref //base year
Let’s run the basic stackedev command:
stackedev Y F_* L_* ref, cohort(first_treat) time(t) never_treat(no_treat) unit_fe(id) clust_unit(id)
which will show this output:
**** Building Stack 24 ****
**** Building Stack 34 ****
**** Building Stack 38 ****
**** Building Stack 56 ****
**** Appending Stacks ****
**** Estimating Model with reghdfe ****
(MWFE estimator converged in 2 iterations)
note: ref omitted because of collinearity
warning: missing F statistic; dropped variables due to collinearity or too few clusters
HDFE Linear regression Number of obs = 3,060
Absorbing 2 HDFE groups F( 91, 49) = .
Statistics robust to heteroskedasticity Prob > F = .
R-squared = 0.9974
Adj R-squared = 0.9970
Within R-sq. = 0.9896
Number of clusters (unit_stack) = 50 Root MSE = 4.1325
(Std. err. adjusted for 50 clusters in unit_stack)
------------------------------------------------------------------------------
| Robust
Y | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
F_2 | .2173356 .429718 0.51 0.615 -.6462151 1.080886
F_3 | .1386198 .4137917 0.33 0.739 -.6929257 .9701654
F_4 | -.0771335 .4251788 -0.18 0.857 -.9315624 .7772953
F_5 | -.3127445 .3628929 -0.86 0.393 -1.042005 .4165161
F_6 | -.4361147 .4095256 -1.06 0.292 -1.259087 .3868578
F_7 | -.3144571 .526613 -0.60 0.553 -1.372726 .7438113
F_8 | -.1044745 .4800377 -0.22 0.829 -1.069146 .8601974
F_9 | -.1156501 .4238786 -0.27 0.786 -.9674661 .7361659
F_10 | .1485313 .4001261 0.37 0.712 -.6555522 .9526148
F_11 | -.2473913 .47278 -0.52 0.603 -1.197478 .7026957
F_12 | -.1452927 .4785819 -0.30 0.763 -1.107039 .8164536
F_13 | .1286208 .4821393 0.27 0.791 -.8402744 1.097516
F_14 | .1440072 .3947098 0.36 0.717 -.6491919 .9372063
F_15 | .3114543 .476962 0.65 0.517 -.6470368 1.269945
F_16 | .1721978 .5333658 0.32 0.748 -.8996409 1.244037
F_17 | -.7585561 .3663682 -2.07 0.044 -1.494801 -.0223118
F_18 | .0699152 .5175897 0.14 0.893 -.9702202 1.110051
F_19 | -.3875619 .2815699 -1.38 0.175 -.9533978 .178274
F_20 | .0638705 .4198326 0.15 0.880 -.7798146 .9075557
F_21 | .035372 .4097576 0.09 0.932 -.7880668 .8588108
F_22 | -.5763502 .4471311 -1.29 0.203 -1.474894 .3221933
F_23 | .1005642 .3572709 0.28 0.780 -.6173985 .818527
F_24 | 4.715301 .9921569 4.75 0.000 2.721487 6.709115
F_25 | 3.848972 .9570067 4.02 0.000 1.925795 5.772149
F_26 | 3.885843 .9759061 3.98 0.000 1.924686 5.846999
F_27 | 3.665572 .9574264 3.83 0.000 1.741551 5.589592
F_28 | 3.629329 .9877625 3.67 0.001 1.644346 5.614312
F_29 | 4.376366 .9749251 4.49 0.000 2.41718 6.335551
F_30 | 4.38623 .9937478 4.41 0.000 2.389219 6.383241
F_31 | 3.95357 .9964798 3.97 0.000 1.951069 5.956071
F_32 | 4.131159 .9566808 4.32 0.000 2.208637 6.053681
F_33 | 4.421442 .9615812 4.60 0.000 2.489073 6.353812
F_34 | 3.870751 1.112861 3.48 0.001 1.634373 6.10713
F_35 | 3.975567 1.073438 3.70 0.001 1.818414 6.132721
F_36 | 3.999014 1.177784 3.40 0.001 1.632169 6.36586
F_37 | 3.909786 .9581247 4.08 0.000 1.984363 5.83521
F_38 | .9388715 .8728544 1.08 0.287 -.8151952 2.692938
F_39 | 1.211486 .7498948 1.62 0.113 -.2954839 2.718456
F_40 | 1.025343 .8874642 1.16 0.254 -.7580827 2.80877
F_41 | 1.526626 .606473 2.52 0.015 .3078726 2.745379
F_42 | 1.434986 .8133947 1.76 0.084 -.199592 3.069564
F_43 | 2.300906 .5985228 3.84 0.000 1.09813 3.503683
F_44 | 2.221324 .7714936 2.88 0.006 .6709491 3.771698
F_45 | .4473907 .6918146 0.65 0.521 -.9428628 1.837644
F_46 | 1.606906 .5320911 3.02 0.004 .537629 2.676183
F_47 | 1.220467 .99843 1.22 0.227 -.7859536 3.226887
F_48 | 1.43739 .6826501 2.11 0.040 .0655537 2.809227
F_49 | 1.691145 .6603621 2.56 0.014 .3640975 3.018192
F_50 | .5937526 .5885007 1.01 0.318 -.5888838 1.776389
F_51 | 1.543893 .5838527 2.64 0.011 .370597 2.717189
F_52 | 1.815931 .5872599 3.09 0.003 .635788 2.996074
F_53 | 1.176133 .679497 1.73 0.090 -.1893669 2.541634
F_54 | 2.117263 .9070423 2.33 0.024 .2944931 3.940032
F_55 | 1.314801 .5422668 2.42 0.019 .2250752 2.404527
L_0 | .0100481 .498877 0.02 0.984 -.9924828 1.012579
L_1 | 8.452976 .4244544 19.91 0.000 7.600003 9.305949
L_2 | 17.61775 .4799071 36.71 0.000 16.65334 18.58216
L_3 | 25.91892 .5228491 49.57 0.000 24.86822 26.96962
L_4 | 34.59866 .8042661 43.02 0.000 32.98243 36.21489
L_5 | 41.79543 1.039745 40.20 0.000 39.70599 43.88488
L_6 | 51.13859 1.248299 40.97 0.000 48.63004 53.64715
L_7 | 59.47399 1.577942 37.69 0.000 56.30299 62.64498
L_8 | 68.24786 1.654616 41.25 0.000 64.92278 71.57293
L_9 | 76.25018 1.923937 39.63 0.000 72.38389 80.11648
L_10 | 84.44234 2.230286 37.86 0.000 79.96041 88.92427
L_11 | 92.93716 2.283911 40.69 0.000 88.34747 97.52685
L_12 | 102.2659 2.653712 38.54 0.000 96.9331 107.5988
L_13 | 109.776 2.872513 38.22 0.000 104.0034 115.5485
L_14 | 118.1795 3.204707 36.88 0.000 111.7394 124.6196
L_15 | 127.3586 3.358882 37.92 0.000 120.6086 134.1085
L_16 | 136.1793 3.598478 37.84 0.000 128.9479 143.4107
L_17 | 144.5375 4.00305 36.11 0.000 136.4931 152.582
L_18 | 153.3496 3.928038 39.04 0.000 145.4559 161.2433
L_19 | 161.894 4.237581 38.20 0.000 153.3783 170.4098
L_20 | 170.1305 4.407902 38.60 0.000 161.2725 178.9885
L_21 | 177.795 4.631182 38.39 0.000 168.4883 187.1017
L_22 | 187.4496 4.907435 38.20 0.000 177.5878 197.3115
L_23 | 202.3896 4.333513 46.70 0.000 193.6811 211.0981
L_24 | 211.301 4.491664 47.04 0.000 202.2746 220.3273
L_25 | 220.5574 4.807412 45.88 0.000 210.8965 230.2182
L_26 | 230.0163 4.752343 48.40 0.000 220.4661 239.5665
L_27 | 258.8725 1.506249 171.87 0.000 255.8456 261.8994
L_28 | 270.2441 1.502177 179.90 0.000 267.2253 273.2628
L_29 | 280.5032 1.489283 188.35 0.000 277.5104 283.496
L_30 | 290.4652 1.590089 182.67 0.000 287.2698 293.6606
L_31 | 298.6743 1.470175 203.16 0.000 295.7199 301.6288
L_32 | 310.7671 1.43449 216.64 0.000 307.8844 313.6498
L_33 | 319.9876 1.486439 215.27 0.000 317.0004 322.9747
L_34 | 330.728 1.523338 217.11 0.000 327.6668 333.7893
L_35 | 339.7674 1.475247 230.31 0.000 336.8028 342.732
L_36 | 349.8512 1.53732 227.57 0.000 346.7618 352.9405
ref | 0 (omitted)
_cons | 47.28654 .5001833 94.54 0.000 46.28138 48.29169
------------------------------------------------------------------------------
Absorbed degrees of freedom:
-----------------------------------------------------+
Absorbed FE | Categories - Redundant = Num. Coefs |
-------------+---------------------------------------|
id#stack | 51 51 0 *|
t#stack | 240 1 239 |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
In order to plot the estimates we can use the event_plot (ssc install event_plot, replace) command where we restrict the figure to 10 leads and lags:
event_plot, default_look graph_opt(xtitle("Periods since the event") ytitle("Average effect") xlabel(-10(1)10) ///
title("stackedev")) stub_lag(L_#) stub_lead(F_#) trimlag(10) trimlead(10) together
And we get:
