{smcl}
{* *! version 2.2.0 Mar 15 2023}{...}
{cmd:help xtevent}
{hline}
{title:Title}
{phang}
{bf:xtevent} {hline 2} Panel Event Study Estimation
{marker syntax}{...}
{title:Syntax}
{pstd}
{p 8 17 2}
{cmd:xtevent}
{depvar} [{indepvars}]
{ifin}
{cmd:,}
{opth pol:icyvar(varname)}
{opth p:anelvar(varname)}
{opth t:imevar(varname)}
[{it:options}]
{synoptset 25 tabbed}{...}
{synopthdr}
{synoptline}
{syntab:Main}
{p2coldent:* {opth pol:icyvar(varname)}} policy variable{p_end}
{synopt: {opth p:anelvar(varname)}} variable that identifies the panels{p_end}
{synopt: {opth t:imevar(varname)}} variable that identifies the time periods{p_end}
{synopt: {opth w:indow(numlist)}} # of periods in the estimation window{p_end}
{synopt: {opth pre(integer)}} # of periods with anticipation effects{p_end}
{synopt: {opth post(integer)}} # of periods with policy effects{p_end}
{synopt: {opth overidpre(integer)}} # of periods to test pre-trends{p_end}
{synopt: {opth overidpost(integer)}} # of periods to test effects leveling off
{p_end}
{synopt:{opth norm(integer)}} event-time coefficient to normalize to 0{p_end}
{synopt:{opth proxy(varname)}} proxy for the confound{p_end}
{synopt:{opt proxyiv(string)}} instruments for the proxy variable{p_end}
{synopt:{opt nofe}} omit panel fixed effects {p_end}
{synopt:{opt note}} omit time fixed effects {p_end}
{synopt: {opt impute(type [, saveimp])}} impute missing values in policyvar{p_end}
{synopt:{opt st:atic}} estimate static model {p_end}
{synopt:{opt diff:avg}} estimate the difference in averages between the post and pre-periods {p_end}
{synopt:{opt tr:end(#1 [, subopt])}} extrapolate linear trend from time period #1 before treatment{p_end}
{synopt:{opt sav:ek(stub [, subopt])}} save time-to-event, event-time, trend, and interaction variables{p_end}
{synopt: {opt kvars(stub)}} use previously generated event-time variables{p_end}
{synopt:{opt reghdfe}} use {help reghdfe} for estimation{p_end}
{synopt:{opt addabsorb(varlist)}} absorb additional variables in {help reghdfe}{p_end}
{synopt:{opt rep:eatedcs}} indicate that the dataset in memory is repeated cross-sectional{p_end}
{synopt:{opt cohort(varname)}} variable that identifies the cohorts for Sun and Abraham (2021) estimation{p_end}
{synopt:{opt control_cohort(varname)}} variable that identifies the control cohort for Sun and Abraham (2021) estimation{p_end}
{synopt:{opt plot}} display plot. See {help xteventplot}.{p_end}
{synopt:{it: additional_options}} additional options to be passed to the estimation command{p_end}
{synoptline}
{p2colreset}{...}
{p 4 6 2} {it: depvar} and {it:indepvars} may contain time-series operators; see{help tsvarlist}.{p_end}
{p 4 6 2} {it: depvar} and {it:indepvars} may contain factor variables; see{help fvvarlist}.{p_end}
{p 4 6 2}* {opt policyvar(varname)} is required. {opt window(integer)} is required unless {opt static}, or {opt pre}, {opt post},
{opt overidpre}, and {opt overidpost} are specified. {opt panelvar(varname)} and {opt timevar(varname)} are required if the data
have not been {cmd:xtset}, otherwise they are optional. See {help xtset}. {p_end}
{p 4 6 2}
See {help xteventtest} for hypothesis testing after estimation and {help xteventplot} for plotting after estimation.{p_end}
{marker description}{...}
{title:Description}
{pstd}
{cmd: xtevent} estimates the effect of a policy variable of interest on a dependent variable using a panel event-study
design. Additional control variables can be included in {it:varlist}. The command allows for estimation when a pre-trend
is present usingthe instrumental variables estimator of Freyaldenhoven et al. (2019). It also allows estimation in
settings with heterogeneous effects by cohort using the Interaction Weighted Estimator of Sun and Abraham (2021).{p_end}
{marker options}{...}
{title:Options}
{dlgtab:Main}
{phang}
{opth policyvar(varname)} specifies the policy variable of interest. {opt policyvar()} is required.
{dlgtab:Estimation}
{phang}
{opth panelvar(varname)} specifies the cross-sectional identifier variable that identifies the panels. {cmd:panelvar()} is required if the data
have not been previously {cmd:xtset}. See {help xtset}.
{phang}
{opth timevar(varname)} specifies the time variable. {cmd:timevar()} is required if the data have not been previously {cmd:xtset}. See
{help xtset}.
{phang}
{opth window(numlist)} specifies the window around the policy change event to estimate dynamic effects. If a single positive integer {it:k}>0
is specified, the estimation will use a symmetric window of {it:k} periods around the event. For example, if {it:k} = 2, there will be five
coefficients in the window (-2,-1,0,1,2) and two endpoints (-3+, 3+). If two distinct integers {it:k1}<=0 and {it:k2}>=0 are specified, the
estimation will use an asymmetric window with {it:k1} periods before the event and {it:k2} periods after the event. For example, with {it:k1} = -1
and {it:k2} = 2, there will be four coefficients in the window (-1,0,1,2) and two endpoints (-2+,3+). {opt window()} is required unless
{opt static} is specified, or if the estimation window is specified using options {opt pre()}, {opt post()}, {opt overidpre()},
and {opt overidpost()} (See below).
{phang}
{opt pre},
{opt post},
{opt overidpre} and
{opt overidpost} offer an alternative way to specify the estimation window:
{phang2} {opt pre} is the number of pre-event periods where anticipation effects are allowed. With {opt window}, {opt pre} is 0.
{phang2} {opt post} is the number of post-event periods where policy effects are allowed. With {opt window}, {opt post} is the number
of periods after the event minus 2.
{phang2} {opt overidpre} is the number of pre-event periods for an overidentification test of pre-trends. With {opt window}, {opt overidpre}
is the number of periods before the event.
{phang2} {opt overidpost} is the number of post-event periods for an overidentification test of effects leveling off. With {opt window},
{opt overidpost} is 2.
{phang} You can specify either {opt window} or
{opt pre},
{opt post},
{opt overidpre} and
{opt overidpost}.
{phang} {opth norm(integer)} specifies the event-time coefficient to be normalized to 0.
The default is to normalize the coefficient on -1.
{phang}
{opth proxy(varlist)} specifies proxy variables for the confound to be included.
{phang}
{opth proxyiv(string)} specifies instruments for the proxy variable for the policy. {opth proxyiv()} admits three syntaxes to use
either leads of the policy variable or aditional variables as instruments. The default is to use leads of the difference of the
policy variable as instruments, selecting the lead with the strongest first stage.
{phang2}
{cmd:proxyiv(select)} selects the lead with the strongest first stage among all possible leads of the differenced policy variable to
be used as an instrument.
{cmd:proxyiv(select)} is the default for the one proxy, one instrument case, and it is only available in this case.
{phang2}
{cmd:proxyiv(# ...)} specifies a numlist with the leads of the differenced policy variable as instruments. For example,
{cmd:proxyiv(1 2)} specifies that the two first leads of the difference of the policy variable will be used as instruments.
{phang2}
{cmd:proxyiv(varlist)} specifies a {it:varlist} with the additional variables to be used as instruments.
{phang}
{opt nofe} excludes panel fixed effects.
{phang}
{opt note} excludes time fixed effects.
{phang}
{opt impute(type [, saveimp])} imputes missing values in {it:policyvar} and uses this new variable as the {it:policyvar} for estimation.
{cmd:type} determines the imputation rule. The suboption {cmd:saveimp} adds the new variable to the database as
{it:policyvar_imputed}. The following imputation types can be implemented:
{phang2}
{cmd:impute(nuchange)} imputes missing values in {it:policyvar} according to {it:no-unobserved change}: It assumes that,
for each unit: i) in periods before the first observed value, the policy value is the same as the first observed value; and
ii) in periods after the last observed value, the policy value is the same as the last observed value.
{phang2}
{cmd:impute(stag)} applies {it:no-unobserved change} if {it:policyvar} satisfies staggered-adoption assumptions for all units:
i) {it:policyvar} must be binary; and ii) once {it:policyvar} reaches the adopted-policy state, it never reverts to the
unadopted-policy state. See Freyaldenhoven et al. (2019) for detailed explanation of the staggered case. Additionally in
the {it:policyvar}, for each unit: i) the first-observed value must be the unadopted-policy-state value, and the last-observed
value must be the adopted-policy-state value; or ii) all policy values in the observed data range must be either
adopted-policy-state values or unadopted-policy-state values.
{phang2}
{cmd:impute(instag)} applies {opt impute(stag)} and additionally imputes missing values inside the observed data range: a missing
value or a group of them will be imputed only if they are both preceded and followed by the unadopted-policy state or by the
adopted-policy state.
{phang}
{opt static} estimates a static panel data model and does not generate or plot event-time dummies. {opt static} is not allowed with
{opt window} or {opt diffavg}.
{phang}
{opt diffavg} calculates the difference in averages between the post-event estimated coefficients and the pre-event estimated
coefficients periods. It also calculates its standard error with {help lincom}. {opt diffavg} is not allowed with {opt static}.
{phang}
{opt tr:end(#1 [, subopt])} extrapolates a linear trend between time periods from period #1 before the policy change, as in
Dobkin et al. (2018). The estimated effect of the policy is the deviation from the extrapolated linear trend. #1 must be less than -1. The
following suboptions can be specified:
{phang2}
{opt method(string)} sets the method to estimate the linear trend. It can be Ordinary Least Squares {opt (ols)} or Generalized Method of
Moments {opt (gmm)}. {opt (ols)} omits the event-time dummies from {opt trend(#1)} to -1 and adds a linear trend (_ttrend) to the regression.
{opt (gmm)} uses the GMM to compute the trend for the event-time dummy coefficients. The default is {opt method(gmm)}.
{phang2}
{opt saveov:erlay} saves estimations for the overlay plot produced by {opt xteventplot, overlay(trend)}.
{phang}
{opt savek(stub [, subopt])} saves variables for time-to-event, event-time, trend, and interaction variables. Event-time dummies are stored as
{it: stub}_eq_m# for the dummy variable # periods before the policy change, and {it:stub}_eq_p# for the dummy variable # periods after the
policy change. The dummy variable for the policy change time is {it:stub}_eq_p0. Event time is stored as {it:stub}_evtime. The trend is stored
as {it:stub}_trend. For estimation with the Sun and Abrahm (2021) method, such that {opt cohort} and {opt control_cohort} are active, the
interaction variables are stored as {it:stub}_m#_c# or {it:stub}_p#_c#, where c# indicates the cohort. The following suboptions can be
specified:
{phang2}
{opt noe:stimate} saves variables for event-time dummies, event-time and trends without estimating the model. This is useful if the
users want to customize their regressions and plots.
{phang2}
{opt saveint:eract} saves interaction variables if {opt cohort} and {opt control_cohort} are specified. {opt noe:stimate} and
{opt saveint:eract} cannot be specified simultaneously.
{phang2}
{opt saveint:eract} saves interaction variables if {opt cohort} and {opt control_cohort} are specified. {opt noe:stimate} and {opt saveint:eract} cannot be specified simultaneously.
{phang}
{opt usek(stub)} uses previously used event-time dummies saved with prefix {it:stub}. This can be used to speed up estimation.
{phang}
{opt reghdfe} uses {help reghdfe} for estimation, instead of {help areg}, {help ivregress}, and {help xtivreg}. {opt reghdfe} is useful for large
datasets. By default, it absorbs the panel fixed effects and the time fixed effects. For OLS estimation, the {opt reghdfe}
option requires {help reghdfe} and {help ftools} to be installed. For IV estimation, it also requires {help ivreghdfe} and {help ivreg2}
to be installed. Note that standard errors may be different and singleton clusters may be dropped using {help reghdfe}.
See Correia (2017).
{phang}
{opt addabsorb(varlist)} specifies additional fixed effects to be absorbed when using {help reghdfe}. By default, {cmd:xtevent} includes time
and unit fixed effects. {opt addabsorb} requires {opt reghdfe}.
{phang}
{opt repeatedcs} indicates that the dataset in memory is repeated cross-sectional. In this case, {opt panelvar} should indicate the groups
at which {opt policyvar} changes. For instance, {opt panelvar} could indicate states at which {opt policyvar} changes, while the observations
in the dataset should be individuals in each state. There is a faster method to estimate the event study in a repeated cross-sectional
dataset, which involves using {cmd:get_unit_time_effects} first, and then {cmd:xtevent}. See {help get_unit_time_effects}. For fixed-effects
estimation, {opt repeatedcs} enables {opt reghdfe}.
{phang}
{opt cohort(varname)} specifies the variable that identifies the cohort for each unit. {opt cohort} and {opt control_cohort} indicate {cmd:xtevent}
to estimate the event-time coefficients with the Interaction-Weighted Estimator proposed by Sun and Abraham (2021). {opt cohort} requires the
Stata module {cmd:avar}; click {stata ssc install avar :here} to install or type "ssc install avar" from inside Stata.
{phang}
{opt control_cohort(varname)} specifies the binary variable that identifies the control cohort. {opt cohort} and {opt control_cohort} indicate
{cmd:xtevent} to estimate the event-time coefficients with the Interaction Weighted Estimator proposed by Sun and Abraham (2021).
{opt control_cohort} requires the Stata module {cmd:avar}; click {stata ssc install avar :here} to install or type "ssc install avar" from
inside Stata.
{phang}
{opt plot} displays a default event-study plot with 95% and sup-t confidence intervals (Montiel Olea and Plagborg-Møller 2019).
Additional options are available with the postestimation command {help xteventplot}.
{phang}
{it: additional_options}: Additional options to be passed to the estimation command. When {opt proxy} is specified, these options are passed
to {help ivregress}. When {opt reghdfe} is specified, these options are passed to {help reghdfe}. Otherwise, they are passed to {help areg} or
to {help regress} if {opt nofe} is specified. This is useful to calculate clustered standard errors or to change regression reporting. Note
that two-way clustering is allowed with {help reghdfe}.
{title:Examples}
{hline}
{pstd}Setup{p_end}
{phang2}{cmd:. {stata webuse nlswork}}{p_end}
{pstd}year variable has many missing observations.{p_end}
{pstd}Create a time variable that ignores these gaps.{p_end}
{phang2}{cmd:. {stata "by idcode (year): gen time=_n"}}{p_end}
{phang2}{cmd:. {stata xtset idcode time}}{p_end}
{pstd}Generate a policy variable that follows staggered adoption.{p_end}
{phang2}{cmd:. {stata "by idcode (time): gen union2 = sum(union)"}}{p_end}
{phang2}{cmd:. {stata replace union2 = 1 if union2 > 1}}{p_end}
{hline}
{pstd}Estimate a basic event study with clustered standard errors.{p_end}
{pstd}Impute the policy variable assuming no unobserved chhanges{p_end}
{phang2}{cmd:. {stata xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure , pol(union2) w(3) cluster(idcode) impute(nuchange)}}
{p_end}
{pstd}Omit unit and time fixed effects{p_end}
{pstd}Impute the policy variable verifying staggered adoption{p_end}
{phang2}{cmd:. {stata xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure , pol(union2) w(3) cluster(idcode) nofe note impute(stag)}}
{p_end}
{pstd}Save event-time dummies without estimating the model{p_end}
{phang2}{cmd:. {stata xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure , pol(union2) w(3) cluster(idcode) impute(stag) savek(a, noe)}}
{p_end}
{pstd}Change the normalized coefficient and use an asymmetric window{p_end}
{phang2}{cmd:. {stata xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure , pol(union2) cluster(idcode) w(-3 1) norm(-2) impute(stag)}}
{p_end}
{pstd}Adjust the pre-trend by estimating a linear trend by GMM {p_end}
{phang2}{cmd:. {stata xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure , pol(union2) w(2) cluster(idcode) trend(-2, method(gmm)) impute(stag)}}
{p_end}
{hline}
{pstd}Freyaldenhoven, Hansen and Shapiro (2019) estimator with proxy variables{p_end}
{phang2}{cmd:. {stata xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure , pol(union2) w(3) vce(cluster idcode) proxy(wks_work) impute(stag)}}
{p_end}
{pstd}Include additional proxy variables, and additional policy variable leads as instruments{p_end}
{phang2}{cmd:. {stata xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure , pol(union2) w(3) vce(cluster idcode) proxy(wks_work hours) proxyiv(1 2) impute(stag)}}
{p_end}
{pstd}{help reghdfe} and two-way clustering {p_end}
{phang2}{cmd:. {stata xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure , pol(union2) w(3) cluster(idcode year) reghdfe proxy(wks_work) impute(stag)}}
{p_end}
{hline}
{pstd}Interaction Weighted Estimator proposed by Sun and Abraham (2021){p_end}
{pstd}First, create the control and control cohort variables{p_end}
{pstd}Generate the variable that indicates cohort{p_end}
{phang2}{cmd:. {stata gen timet=year if union2==1}}
{p_end}
{phang2}{cmd:. {stata "by idcode: egen time_of_treat=min(timet)"}}
{p_end}
{pstd}Generate the variable that indicates the control cohort. We use the never-treated units as the control cohort{p_end}
{phang2}{cmd:. {stata gen never_treat=time_of_treat==.}}
{p_end}
{phang2}{cmd:. {stata xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure, policyvar(union2) window(3) impute(stag) vce(cluster idcode) cohort(time_of_treat) control_cohort(never_treat)}}
{p_end}
{marker saved}{...}
{title:Saved Results}
{pstd}
{cmd:xtevent} saves the following in {cmd:e()}:
{synoptset 20 tabbed}{...}
{p2col 5 20 24 2: Scalars}{p_end}
{synopt:{cmd:e(lwindow)}}left endpoint for estimation window{p_end}
{synopt:{cmd:e(rwindow)}}right endpoint for estimation window{p_end}
{synoptset 20 tabbed}{...}
{p2col 5 20 24 2: Macros}{p_end}
{synopt:{cmd:e(names)}}names of the variables for the event-time dummies{p_end}
{synopt:{cmd:e(y1)}}mean of dependent variable et event-time = -1{p_end}
{synopt:{cmd:e(x1)}}mean of proxy variable et event-time = -1, when only one proxy is specified{p_end}
{synopt:{cmd:e(trend)}}"trend" if estimation included extrapolation of a linear trend{p_end}
{synopt:{cmd:e(cmd)}}estimation command: can be {help regress}, {help areg}, {help ivregress}, {help xtivreg}, or {help reghdfe}
{p_end}
{synopt:{cmd:e(df)}}degrees of freedom{p_end}
{synopt:{cmd:e(komit)}}list of lags/leads omitted from regression{p_end}
{synopt:{cmd:e(kmiss)}}list of lags/leads to be omitted from plot{p_end}
{synopt:{cmd:e(method)}}"ols" or "iv"{p_end}
{synopt:{cmd:e(cmd2)}}"xtevent"{p_end}
{synopt:{cmd:e(depvar)}}dependent variable{p_end}
{synopt:{cmd:e(pre)}}number of periods with anticipation effects{p_end}
{synopt:{cmd:e(post)}}number of periods with policy effects{p_end}
{synopt:{cmd:e(overidpre)}}number of periods to test for pre-trends{p_end}
{synopt:{cmd:e(overidpost)}}number of periods to test for effects leveling off{p_end}
{synopt:{cmd:e(stub)}}prefix for saved event-time dummy variables{p_end}
{synoptset 20 tabbed}{...}
{p2col 5 20 24 2: Matrices}{p_end}
{synopt:{cmd:e(b)}}coefficient vector{p_end}
{synopt:{cmd:e(V)}}variance-covariance matrix{p_end}
{synopt:{cmd:e(delta)}}coefficient vector of event-time dummies{p_end}
{synopt:{cmd:e(Vdelta)}}variance-covariance matrix of the event-time dummies coefficients{p_end}
{synopt:{cmd:e(deltax)}} coefficients for proxy event-study to be used in overlay plot{p_end}
{synopt:{cmd:e(deltaxsc)}}scaled coefficients for proxy event-study to be used in overlay plot{p_end}
{synopt:{cmd:e(deltaov)}}coefficients for event-study to be used in overlay plot{p_end}
{synopt:{cmd:e(Vdeltax)}} variance-covariance matrix of proxy-event study coefficients for overlay plot{p_end}
{synopt:{cmd:e(Vdeltax)}} variance-covariance matrix of event-study coefficients for overlay plot{p_end}
{synopt:{cmd:e(mattrendy)}} matrix with y-axis values of trend for overlay plot, only when {opt trend(#1)} is specified{p_end}
{synopt:{cmd:e(mattrendx)}} matrix with x-axis values of trend for overlay plot, only when {opt trend(#1)} is specified{p_end}
{synopt:{cmd:e(Sigma_ff)}} variance estimate of the cohort share estimators, only when {opt cohort} and {opt control_cohort} are specified{p_end}
{synopt:{cmd:e(ff_w)}} Each column vector contains estimates of cohort shares underlying the given relative time, only when {opt cohort} and {opt control_cohort} are specified{p_end}
{synopt:{cmd:e(V_interact)}} each column vector contains variance estimate of the cohort-specific effect estimator for the given relative time, only when {opt cohort} and {opt control_cohort} are specified{p_end}
{synopt:{cmd:e(b_interact)}} each column vector contains estimates of cohort-specific effect for the given relative time, only when {opt cohort} and {opt control_cohort} are specified{p_end}
{synoptset 20 tabbed}{...}
{p2col 5 20 24 2: Functions}{p_end}
{synopt:{cmd:e(sample)}}marks estimation sample{p_end}
{title:Authors}
{pstd}Simon Freyaldenhoven, Federal Reserve Bank of Philadelphia.{p_end}
simon.freyaldenhoven@phil.frb.org
{pstd}Christian Hansen, University of Chicago, Booth School of Business.{p_end}
chansen1@chicagobooth.edu
{pstd}Jorge Pérez Pérez, Banco de México.{p_end}
jorgepp@banxico.org.mx
{pstd}Jesse Shapiro, Harvard University and NBER.{p_end}
jesse_shapiro@fas.harvard.edu
{title:Support}
{pstd}For support and to report bugs please email Jorge Pérez Pérez, Banco de México.{break}
jorgepp@banxico.org.mx
{pstd}{cmd:xtevent} can also be found on {browse "https://github.com/JMSLab/xtevent":GitHub}.
{title:References}
{pstd}Correia, S. (2017) . "Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator" Working Paper. {browse "http://scorreia.com/research/hdfe.pdf"}
{pstd}Dobkin, C., Finkelstein A., Kluender. R., and Notowidigdo, M. J. (2018) "The Economic Consequences of Hospital Admissions."
{it:American Economic Review}, 108 (2): 308-52.
{pstd}Freyaldenhoven, S., Hansen, C. and Shapiro, J. (2019) "Pre-event Trends in the Panel Event-study Design" {it:American Economic Review}, 109 (9):
3307-38.
{pstd}Freyaldenhoven, S., Hansen, C., Pérez Pérez, J. and Shapiro, J. (2021) "Visualization, Identification,
and Estimation in the Linear Panel Event-study Design". Working paper.
{pstd}Montiel Olea, J.L. and Plagborg-Møller, M. (2019) "Simultaneous confidence bands: Theory, implementation, and an application to SVARs".
{it:Journal of Applied Econometrics}, 34: 1– 17.
{pstd}Sun, L. and Abraham, S. (2021) "Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects".
{it:Journal of Econometrics}, 225 (2): 175-199.
{title:Acknowledgements}
{pstd}We are grateful to Veli Andirin, Mauricio Cáceres, Richard Calvo, Constantino Carreto, Kathryn Dawson-Townsend, Theresa Doppstadt,
Ángel Espinoza, Miguel Fajardo-Steinhauser Samuele Giambra, Ray Huang, Chandra Kant Dhakal, Panagiotis Konstantinou, Per Lidbom,
Isabel Z. Martínez, Diego Mayorga, Eric Melse, Stefano Molina, Asjad Naqvi, Anna Pasnau, Nathan Schor, Emily Wang, Matthias Weigand,
and Wenli Xu for contributions to development and for testing earlier versions of this command.