stackedev

2023-03-14 21:48| 来源: 网络整理| 查看: 265

stackedev (Cengiz, Dube, Lindner, Zipperer 2019) Table of contents Introduction Installation and options The options Generate sample data Test the command Introduction

The stackedev command is written by Joshua Bleiberg based on the Cengiz, Dube, Lindner, Zipperer 2019 QJE paper The effect of minimum wages on low-wage jobs.

The command is currently under active development so options might change around.

Installation and options

Install the command from SSC:

ssc install stackedev, replace

Take a look at the help file:

help stackedev

The core syntax is as follows:

stackedev Y F* L* , cohort(first_treat) time(t) never_treat(no_treat) unit_fe(i) clust_unit(i)

where:

Variable Description Y outcome variable i panel id t time variable lags manually generated lag variables leads manually generated lead variables first_treat Year of first treatment no_treat Dummy = 1 if unit is never treated The options Option Description

INCOMPLETE

Generate sample data

Here we generate a test dataset with heterogeneous treatments:

clear local units = 30 local start = 1 local end = 60 local time = `end' - `start' + 1 local obsv = `units' * `time' set obs `obsv' egen id = seq(), b(`time') egen t = seq(), f(`start') t(`end') sort id t xtset id t set seed 20211222 gen Y = 0 // outcome variable gen D = 0 // intervention variable gen cohort = . // treatment cohort gen effect = . // treatment effect size gen first_treat = . // when the treatment happens for each cohort gen rel_time = . // time - first_treat levelsof id, local(lvls) foreach x of local lvls { local chrt = runiformint(0,5) replace cohort = `chrt' if id==`x' } levelsof cohort , local(lvls) foreach x of local lvls { local eff = runiformint(2,10) replace effect = `eff' if cohort==`x' local timing = runiformint(`start',`end' + 20) // replace first_treat = `timing' if cohort==`x' replace first_treat = . if first_treat > `end' replace D = 1 if cohort==`x' & t>= `timing' } replace rel_time = t - first_treat replace Y = id + t + cond(D==1, effect * rel_time, 0) + rnormal()

Generate the graph:

xtline Y, overlay legend(off)

Test the command

For stackedev we need to generate the no_treat variable and 10 leads and lags:

gen no_treat = first_treat==. summ rel_time local relmin = abs(r(min)) local relmax = abs(r(max)) // leads cap drop F_* forval x = 1/`relmin' { // drop the first lead gen F_`x' = rel_time == -`x' replace F_`x' = 0 if no_treat==1 } //lags cap drop L_* forval x = 0/`relmax' { gen L_`x' = rel_time == `x' replace L_`x' = 0 if no_treat==1 } ren F_1 ref //base year

Let鈥檚 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 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 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:

INCOMPLETE

Content by Asjad Naqvi (2020-2022). Distributed under an MIT license.

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stackedev

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