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: Nov 2024
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)
warning: missing F statistic; dropped variables due to collinearity or too few clusters
note: ref omitted because of collinearity
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 = 4.1332
(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
<OUTPUT TRUNCATED>
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 0 240 |
-----------------------------------------------------+
* = 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:
