Title: Treatment Effects with Multiple Periods and Groups
Version: 2.1.2
URL: https://bcallaway11.github.io/did/, https://github.com/bcallaway11/did/
Description: The standard Difference-in-Differences (DID) setup involves two periods and two groups – a treated group and untreated group. Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time. This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>. The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.
Depends: R (≥ 3.5),
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: BMisc (≥ 1.4.4), Matrix, pbapply, ggplot2, ggpubr, DRDID, generics, methods, tidyr
RoxygenNote: 7.2.1
VignetteBuilder: knitr
Suggests: rmarkdown, plm, here, knitr, covr
NeedsCompilation: no
Packaged: 2022-07-20 03:09:38 UTC; brant
Author: Brantly Callaway [aut, cre], Pedro H. C. Sant'Anna [aut]
Maintainer: Brantly Callaway <brantly.callaway@uga.edu>
Repository: CRAN
Date/Publication: 2022-07-20 16:00:05 UTC

AGGTEobj

Description

Objects of this class hold results on aggregated group-time average treatment effects

An object for holding aggregated treatment effect parameters.

Usage

AGGTEobj(
  overall.att = NULL,
  overall.se = NULL,
  type = "simple",
  egt = NULL,
  att.egt = NULL,
  se.egt = NULL,
  crit.val.egt = NULL,
  inf.function = NULL,
  min_e = NULL,
  max_e = NULL,
  balance_e = NULL,
  call = NULL,
  DIDparams = NULL
)

Arguments

overall.att

The estimated overall ATT

overall.se

Standard error for overall ATT

type

Which type of aggregated treatment effect parameter to compute. One option is "simple" (this just computes a weighted average of all group-time average treatment effects with weights proportional to group size). Other options are "dynamic" (this computes average effects across different lengths of exposure to the treatment and is similar to an "event study"; here the overall effect averages the effect of the treatment across all positive lengths of exposure); "group" (this is the default option and computes average treatment effects across different groups; here the overall effect averages the effect across different groups); and "calendar" (this computes average treatment effects across different time periods; here the overall effect averages the effect across each time period).

egt

Holds the length of exposure (for dynamic effects), the group (for selective treatment timing), or the time period (for calendar time effects)

att.egt

The ATT specific to egt

se.egt

The standard error specific to egt

crit.val.egt

A critical value for computing uniform confidence bands for dynamic effects, selective treatment timing, or time period effects.

inf.function

The influence function of the chosen aggregated parameters

min_e

For event studies, this is the smallest event time to compute dynamic effects for. By default, min_e = -Inf so that effects at all lengths of exposure are computed.

max_e

For event studies, this is the largest event time to compute dynamic effects for. By default, max_e = Inf so that effects at all lengths of exposure are computed.

balance_e

If set (and if one computes dynamic effects), it balances the sample with respect to event time. For example, if balance.e=2, aggte will drop groups that are not exposed to treatment for at least three periods. (the initial period when e=0 as well as the next two periods when e=1 and the e=2). This ensures that the composition of groups does not change when event time changes.

call

The function call to aggte

DIDparams

A DIDparams object

Value

an AGGTEobj

DIDparams

Description

Object to hold did parameters that are passed across functions

Usage

DIDparams(
  yname,
  tname,
  idname = NULL,
  gname,
  xformla = NULL,
  data,
  control_group,
  anticipation = 0,
  weightsname = NULL,
  alp = 0.05,
  bstrap = TRUE,
  biters = 1000,
  clustervars = NULL,
  cband = TRUE,
  print_details = TRUE,
  pl = FALSE,
  cores = 1,
  est_method = "dr",
  base_period = "varying",
  panel = TRUE,
  true_repeated_cross_sections,
  n = NULL,
  nG = NULL,
  nT = NULL,
  tlist = NULL,
  glist = NULL,
  call = NULL
)

Arguments

yname

The name of the outcome variable

tname

The name of the column containing the time periods

idname

The individual (cross-sectional unit) id name

gname

The name of the variable in data that contains the first period when a particular observation is treated. This should be a positive number for all observations in treated groups. It defines which "group" a unit belongs to. It should be 0 for units in the untreated group.

xformla

A formula for the covariates to include in the model. It should be of the form ~ X1 + X2. Default is NULL which is equivalent to xformla=~1. This is used to create a matrix of covariates which is then passed to the 2x2 DID estimator chosen in est_method.

data

The name of the data.frame that contains the data

control_group

Which units to use the control group. The default is "nevertreated" which sets the control group to be the group of units that never participate in the treatment. This group does not change across groups or time periods. The other option is to set group="notyettreated". In this case, the control group is set to the group of units that have not yet participated in the treatment in that time period. This includes all never treated units, but it includes additional units that eventually participate in the treatment, but have not participated yet.

anticipation

The number of time periods before participating in the treatment where units can anticipate participating in the treatment and therefore it can affect their untreated potential outcomes

weightsname

The name of the column containing the sampling weights. If not set, all observations have same weight.

alp

the significance level, default is 0.05

bstrap

Boolean for whether or not to compute standard errors using the multiplier bootstrap. If standard errors are clustered, then one must set bstrap=TRUE. Default is TRUE (in addition, cband is also by default TRUE indicating that uniform confidence bands will be returned. If bstrap is FALSE, then analytical standard errors are reported.

biters

The number of bootstrap iterations to use. The default is 1000, and this is only applicable if bstrap=TRUE.

clustervars

A vector of variables names to cluster on. At most, there can be two variables (otherwise will throw an error) and one of these must be the same as idname which allows for clustering at the individual level. By default, we cluster at individual level (when bstrap=TRUE).

cband

Boolean for whether or not to compute a uniform confidence band that covers all of the group-time average treatment effects with fixed probability 1-alp. In order to compute uniform confidence bands, bstrap must also be set to TRUE. The default is TRUE.

print_details

Whether or not to show details/progress of computations. Default is FALSE.

pl

Whether or not to use parallel processing

cores

The number of cores to use for parallel processing

est_method

the method to compute group-time average treatment effects. The default is "dr" which uses the doubly robust approach in the DRDID package. Other built-in methods include "ipw" for inverse probability weighting and "reg" for first step regression estimators. The user can also pass their own function for estimating group time average treatment effects. This should be a function f(Y1,Y0,treat,covariates) where Y1 is an n x 1 vector of outcomes in the post-treatment outcomes, Y0 is an n x 1 vector of pre-treatment outcomes, treat is a vector indicating whether or not an individual participates in the treatment, and covariates is an n x k matrix of covariates. The function should return a list that includes ATT (an estimated average treatment effect), and inf.func (an n x 1 influence function). The function can return other things as well, but these are the only two that are required. est_method is only used if covariates are included.

base_period

Whether to use a "varying" base period or a "universal" base period. Either choice results in the same post-treatment estimates of ATT(g,t)'s. In pre-treatment periods, using a varying base period amounts to computing a pseudo-ATT in each treatment period by comparing the change in outcomes for a particular group relative to its comparison group in the pre-treatment periods (i.e., in pre-treatment periods this setting computes changes from period t-1 to period t, but repeatedly changes the value of t)

A universal base period fixes the base period to always be (g-anticipation-1). This does not compute pseudo-ATT(g,t)'s in pre-treatment periods, but rather reports average changes in outcomes from period t to (g-anticipation-1) for a particular group relative to its comparison group. This is analogous to what is often reported in event study regressions.

Using a varying base period results in an estimate of ATT(g,t) being reported in the period immediately before treatment. Using a universal base period normalizes the estimate in the period right before treatment (or earlier when the user allows for anticipation) to be equal to 0, but one extra estimate in an earlier period.

panel

Whether or not the data is a panel dataset. The panel dataset should be provided in long format – that is, where each row corresponds to a unit observed at a particular point in time. The default is TRUE. When is using a panel dataset, the variable idname must be set. When panel=FALSE, the data is treated as repeated cross sections.

true_repeated_cross_sections

Whether or not the data really is repeated cross sections. (We include this because unbalanced panel code runs through the repeated cross sections code)

n

The number of observations. This is equal to the number of units (which may be different from the number of rows in a panel dataset).

nG

The number of groups

nT

The number of time periods

tlist

a vector containing each time period

glist

a vector containing each group

call

Function call to att_gt

MP

Description

Multi-period objects that hold results for group-time average treatment effects

Usage

MP(
  group,
  t,
  att,
  V_analytical,
  se,
  c,
  inffunc,
  n = NULL,
  W = NULL,
  Wpval = NULL,
  aggte = NULL,
  alp = 0.05,
  DIDparams = NULL
)

Arguments

group

which group (defined by period first treated) an group-time average treatment effect is for

t

which time period a group-time average treatment effect is for

att

the group-average treatment effect for group group and time period t

V_analytical

Analytical estimator for the asymptotic variance-covariance matrix for group-time average treatment effects

se

standard errors for group-time average treatment effects. If bootstrap is set to TRUE, this provides bootstrap-based se.

c

simultaneous critical value if one is obtaining simultaneous confidence bands. Otherwise it reports the critical value based on pointwise normal approximation.

inffunc

the influence function for estimating group-time average treatment effects

n

the number of unique cross-sectional units (unique values of idname)

W

the Wald statistic for pre-testing the common trends assumption

Wpval

the p-value of the Wald statistic for pre-testing the common trends assumption

aggte

an aggregate treatment effects object

alp

the significance level, default is 0.05

DIDparams

a DIDparams object. A way to optionally return the parameters of the call to att_gt() or conditional_did_pretest().

Value

MP object

MP.TEST

Description

An object that holds results from computing pre-test of the conditional parallel trends assumption

Usage

MP.TEST(
  CvM = NULL,
  CvMb = NULL,
  CvMcval = NULL,
  CvMpval = NULL,
  KS = NULL,
  KSb = NULL,
  KScval = NULL,
  KSpval = NULL,
  clustervars = NULL,
  xformla = NULL
)

Arguments

CvM

Cramer von Mises test statistic

CvMb

a vector of bootstrapped Cramer von Mises test statistics

CvMcval

CvM critical value

CvMpval

p-value for CvM test

KS

Kolmogorov-Smirnov test statistic

KSb

a vector of bootstrapped KS test statistics

KScval

KS critical value

KSpval

p-value for KS test

clustervars

vector of which variables were clustered on for the test

xformla

formla for the X variables used in the test

Aggregate Group-Time Average Treatment Effects

Description

A function to take group-time average treatment effects and aggregate them into a smaller number of parameters. There are several possible aggregations including "simple", "dynamic", "group", and "calendar."

Usage

aggte(
  MP,
  type = "group",
  balance_e = NULL,
  min_e = -Inf,
  max_e = Inf,
  na.rm = FALSE,
  bstrap = NULL,
  biters = NULL,
  cband = NULL,
  alp = NULL,
  clustervars = NULL
)

Arguments

MP

an MP object (i.e., the results of the att_gt() method)

type

Which type of aggregated treatment effect parameter to compute. One option is "simple" (this just computes a weighted average of all group-time average treatment effects with weights proportional to group size). Other options are "dynamic" (this computes average effects across different lengths of exposure to the treatment and is similar to an "event study"; here the overall effect averages the effect of the treatment across all positive lengths of exposure); "group" (this is the default option and computes average treatment effects across different groups; here the overall effect averages the effect across different groups); and "calendar" (this computes average treatment effects across different time periods; here the overall effect averages the effect across each time period).

balance_e

If set (and if one computes dynamic effects), it balances the sample with respect to event time. For example, if balance.e=2, aggte will drop groups that are not exposed to treatment for at least three periods. (the initial period when e=0 as well as the next two periods when e=1 and the e=2). This ensures that the composition of groups does not change when event time changes.

min_e

For event studies, this is the smallest event time to compute dynamic effects for. By default, min_e = -Inf so that effects at all lengths of exposure are computed.

max_e

For event studies, this is the largest event time to compute dynamic effects for. By default, max_e = Inf so that effects at all lengths of exposure are computed.

na.rm

Logical value if we are to remove missing Values from analyses. Defaults is FALSE.

bstrap

Boolean for whether or not to compute standard errors using the multiplier bootstrap. If standard errors are clustered, then one must set bstrap=TRUE. Default is value set in the MP object. If bstrap is FALSE, then analytical standard errors are reported.

biters

The number of bootstrap iterations to use. The default is the value set in the MP object, and this is only applicable if bstrap=TRUE.

cband

Boolean for whether or not to compute a uniform confidence band that covers all of the group-time average treatment effects with fixed probability 1-alp. In order to compute uniform confidence bands, bstrap must also be set to TRUE. The default is the value set in the MP object

alp

the significance level, default is value set in the MP object.

clustervars

A vector of variables to cluster on. At most, there can be two variables (otherwise will throw an error) and one of these must be the same as idname which allows for clustering at the individual level. Default is the variables set in the MP object

Value

An AGGTEobj object that holds the results from the aggregation

Examples

Initial ATT(g,t) estimates from att_gt() 

data(mpdta)
out <- att_gt(yname="lemp",
               tname="year",
               idname="countyreal",
               gname="first.treat",
               xformla=NULL,
               data=mpdta)

You can aggregate the ATT(g,t) in many ways.

Overall ATT: 

aggte(out, type = "simple")
#> 
#> Call:
#> aggte(MP = out, type = "simple")
#> 
#> Reference: Callaway, Brantly and Pedro H.C. Sant'Anna.  "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015> 
#> 
#> 
#>    ATT    Std. Error     [ 95%  Conf. Int.]  
#>  -0.04         0.013    -0.0654     -0.0145 *
#> 
#> 
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#> 
#> Control Group:  Never Treated,  Anticipation Periods:  0
#> Estimation Method:  Doubly Robust

Dynamic ATT (Event-Study): 

aggte(out, type = "dynamic")
#> 
#> Call:
#> aggte(MP = out, type = "dynamic")
#> 
#> Reference: Callaway, Brantly and Pedro H.C. Sant'Anna.  "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015> 
#> 
#> 
#> Overall summary of ATT's based on event-study/dynamic aggregation:  
#>      ATT    Std. Error     [ 95%  Conf. Int.]  
#>  -0.0772          0.02    -0.1165      -0.038 *
#> 
#> 
#> Dynamic Effects:
#>  Event time Estimate Std. Error [95% Simult.  Conf. Band]  
#>          -3   0.0305     0.0150       -0.0060      0.0670  
#>          -2  -0.0006     0.0139       -0.0344      0.0333  
#>          -1  -0.0245     0.0150       -0.0610      0.0121  
#>           0  -0.0199     0.0117       -0.0485      0.0087  
#>           1  -0.0510     0.0168       -0.0919     -0.0100 *
#>           2  -0.1373     0.0380       -0.2299     -0.0446 *
#>           3  -0.1008     0.0360       -0.1887     -0.0130 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#> 
#> Control Group:  Never Treated,  Anticipation Periods:  0
#> Estimation Method:  Doubly Robust

ATT for each group: 

aggte(out, type = "group")
#> 
#> Call:
#> aggte(MP = out, type = "group")
#> 
#> Reference: Callaway, Brantly and Pedro H.C. Sant'Anna.  "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015> 
#> 
#> 
#> Overall summary of ATT's based on group/cohort aggregation:  
#>     ATT    Std. Error     [ 95%  Conf. Int.]  
#>  -0.031        0.0127    -0.0558     -0.0062 *
#> 
#> 
#> Group Effects:
#>  Group Estimate Std. Error [95% Simult.  Conf. Band]  
#>   2004  -0.0797     0.0308       -0.1461     -0.0134 *
#>   2006  -0.0229     0.0175       -0.0606      0.0148  
#>   2007  -0.0261     0.0163       -0.0612      0.0091  
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#> 
#> Control Group:  Never Treated,  Anticipation Periods:  0
#> Estimation Method:  Doubly Robust

ATT for each calendar year: 

aggte(out, type = "calendar")
#> 
#> Call:
#> aggte(MP = out, type = "calendar")
#> 
#> Reference: Callaway, Brantly and Pedro H.C. Sant'Anna.  "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015> 
#> 
#> 
#> Overall summary of ATT's based on calendar time aggregation:  
#>      ATT    Std. Error     [ 95%  Conf. Int.]  
#>  -0.0417        0.0177    -0.0765     -0.0069 *
#> 
#> 
#> Time Effects:
#>  Time Estimate Std. Error [95% Simult.  Conf. Band]  
#>  2004  -0.0105     0.0248       -0.0689      0.0479  
#>  2005  -0.0704     0.0313       -0.1443      0.0035  
#>  2006  -0.0488     0.0199       -0.0956     -0.0020 *
#>  2007  -0.0371     0.0143       -0.0708     -0.0033 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#> 
#> Control Group:  Never Treated,  Anticipation Periods:  0
#> Estimation Method:  Doubly Robust

Group-Time Average Treatment Effects

Description

att_gt computes average treatment effects in DID setups where there are more than two periods of data and allowing for treatment to occur at different points in time and allowing for treatment effect heterogeneity and dynamics. See Callaway and Sant'Anna (2021) for a detailed description.

Usage

att_gt(
  yname,
  tname,
  idname = NULL,
  gname,
  xformla = NULL,
  data,
  panel = TRUE,
  allow_unbalanced_panel = FALSE,
  control_group = c("nevertreated", "notyettreated"),
  anticipation = 0,
  weightsname = NULL,
  alp = 0.05,
  bstrap = TRUE,
  cband = TRUE,
  biters = 1000,
  clustervars = NULL,
  est_method = "dr",
  base_period = "varying",
  print_details = FALSE,
  pl = FALSE,
  cores = 1
)

Arguments

yname

The name of the outcome variable

tname

The name of the column containing the time periods

idname

The individual (cross-sectional unit) id name

gname

The name of the variable in data that contains the first period when a particular observation is treated. This should be a positive number for all observations in treated groups. It defines which "group" a unit belongs to. It should be 0 for units in the untreated group.

xformla

A formula for the covariates to include in the model. It should be of the form ~ X1 + X2. Default is NULL which is equivalent to xformla=~1. This is used to create a matrix of covariates which is then passed to the 2x2 DID estimator chosen in est_method.

data

The name of the data.frame that contains the data

panel

Whether or not the data is a panel dataset. The panel dataset should be provided in long format – that is, where each row corresponds to a unit observed at a particular point in time. The default is TRUE. When is using a panel dataset, the variable idname must be set. When panel=FALSE, the data is treated as repeated cross sections.

allow_unbalanced_panel

Whether or not function should "balance" the panel with respect to time and id. The default values if FALSE which means that att_gt() will drop all units where data is not observed in all periods. The advantage of this is that the computations are faster (sometimes substantially).

control_group

Which units to use the control group. The default is "nevertreated" which sets the control group to be the group of units that never participate in the treatment. This group does not change across groups or time periods. The other option is to set group="notyettreated". In this case, the control group is set to the group of units that have not yet participated in the treatment in that time period. This includes all never treated units, but it includes additional units that eventually participate in the treatment, but have not participated yet.

anticipation

The number of time periods before participating in the treatment where units can anticipate participating in the treatment and therefore it can affect their untreated potential outcomes

weightsname

The name of the column containing the sampling weights. If not set, all observations have same weight.

alp

the significance level, default is 0.05

bstrap

Boolean for whether or not to compute standard errors using the multiplier bootstrap. If standard errors are clustered, then one must set bstrap=TRUE. Default is TRUE (in addition, cband is also by default TRUE indicating that uniform confidence bands will be returned. If bstrap is FALSE, then analytical standard errors are reported.

cband

Boolean for whether or not to compute a uniform confidence band that covers all of the group-time average treatment effects with fixed probability 1-alp. In order to compute uniform confidence bands, bstrap must also be set to TRUE. The default is TRUE.

biters

The number of bootstrap iterations to use. The default is 1000, and this is only applicable if bstrap=TRUE.

clustervars

A vector of variables names to cluster on. At most, there can be two variables (otherwise will throw an error) and one of these must be the same as idname which allows for clustering at the individual level. By default, we cluster at individual level (when bstrap=TRUE).

est_method

the method to compute group-time average treatment effects. The default is "dr" which uses the doubly robust approach in the DRDID package. Other built-in methods include "ipw" for inverse probability weighting and "reg" for first step regression estimators. The user can also pass their own function for estimating group time average treatment effects. This should be a function f(Y1,Y0,treat,covariates) where Y1 is an n x 1 vector of outcomes in the post-treatment outcomes, Y0 is an n x 1 vector of pre-treatment outcomes, treat is a vector indicating whether or not an individual participates in the treatment, and covariates is an n x k matrix of covariates. The function should return a list that includes ATT (an estimated average treatment effect), and inf.func (an n x 1 influence function). The function can return other things as well, but these are the only two that are required. est_method is only used if covariates are included.

base_period

Whether to use a "varying" base period or a "universal" base period. Either choice results in the same post-treatment estimates of ATT(g,t)'s. In pre-treatment periods, using a varying base period amounts to computing a pseudo-ATT in each treatment period by comparing the change in outcomes for a particular group relative to its comparison group in the pre-treatment periods (i.e., in pre-treatment periods this setting computes changes from period t-1 to period t, but repeatedly changes the value of t)

A universal base period fixes the base period to always be (g-anticipation-1). This does not compute pseudo-ATT(g,t)'s in pre-treatment periods, but rather reports average changes in outcomes from period t to (g-anticipation-1) for a particular group relative to its comparison group. This is analogous to what is often reported in event study regressions.

Using a varying base period results in an estimate of ATT(g,t) being reported in the period immediately before treatment. Using a universal base period normalizes the estimate in the period right before treatment (or earlier when the user allows for anticipation) to be equal to 0, but one extra estimate in an earlier period.

print_details

Whether or not to show details/progress of computations. Default is FALSE.

pl

Whether or not to use parallel processing

cores

The number of cores to use for parallel processing

Value

an MP object containing all the results for group-time average treatment effects

Examples:

Basic att_gt() call: 

# Example data
data(mpdta)

out1 <- att_gt(yname="lemp",
               tname="year",
               idname="countyreal",
               gname="first.treat",
               xformla=NULL,
               data=mpdta)
summary(out1)
#> 
#> Call:
#> att_gt(yname = "lemp", tname = "year", idname = "countyreal", 
#>     gname = "first.treat", xformla = NULL, data = mpdta)
#> 
#> Reference: Callaway, Brantly and Pedro H.C. Sant'Anna.  "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015> 
#> 
#> Group-Time Average Treatment Effects:
#>  Group Time ATT(g,t) Std. Error [95% Simult.  Conf. Band]  
#>   2004 2004  -0.0105     0.0235       -0.0752      0.0542  
#>   2004 2005  -0.0704     0.0307       -0.1549      0.0140  
#>   2004 2006  -0.1373     0.0365       -0.2379     -0.0367 *
#>   2004 2007  -0.1008     0.0383       -0.2062      0.0046  
#>   2006 2004   0.0065     0.0236       -0.0585      0.0715  
#>   2006 2005  -0.0028     0.0195       -0.0564      0.0509  
#>   2006 2006  -0.0046     0.0185       -0.0556      0.0464  
#>   2006 2007  -0.0412     0.0202       -0.0969      0.0145  
#>   2007 2004   0.0305     0.0155       -0.0122      0.0733  
#>   2007 2005  -0.0027     0.0158       -0.0462      0.0408  
#>   2007 2006  -0.0311     0.0176       -0.0794      0.0173  
#>   2007 2007  -0.0261     0.0167       -0.0720      0.0199  
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#> 
#> P-value for pre-test of parallel trends assumption:  0.16812
#> Control Group:  Never Treated,  Anticipation Periods:  0
#> Estimation Method:  Doubly Robust

Using covariates: 

out2 <- att_gt(yname="lemp",
               tname="year",
               idname="countyreal",
               gname="first.treat",
               xformla=~lpop,
               data=mpdta)
summary(out2)
#> 
#> Call:
#> att_gt(yname = "lemp", tname = "year", idname = "countyreal", 
#>     gname = "first.treat", xformla = ~lpop, data = mpdta)
#> 
#> Reference: Callaway, Brantly and Pedro H.C. Sant'Anna.  "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015> 
#> 
#> Group-Time Average Treatment Effects:
#>  Group Time ATT(g,t) Std. Error [95% Simult.  Conf. Band]  
#>   2004 2004  -0.0145     0.0233       -0.0759      0.0469  
#>   2004 2005  -0.0764     0.0297       -0.1546      0.0018  
#>   2004 2006  -0.1404     0.0348       -0.2321     -0.0488 *
#>   2004 2007  -0.1069     0.0340       -0.1964     -0.0174 *
#>   2006 2004  -0.0005     0.0236       -0.0627      0.0618  
#>   2006 2005  -0.0062     0.0184       -0.0548      0.0424  
#>   2006 2006   0.0010     0.0194       -0.0502      0.0521  
#>   2006 2007  -0.0413     0.0189       -0.0912      0.0086  
#>   2007 2004   0.0267     0.0145       -0.0115      0.0650  
#>   2007 2005  -0.0046     0.0163       -0.0476      0.0384  
#>   2007 2006  -0.0284     0.0191       -0.0788      0.0219  
#>   2007 2007  -0.0288     0.0166       -0.0724      0.0149  
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#> 
#> P-value for pre-test of parallel trends assumption:  0.23267
#> Control Group:  Never Treated,  Anticipation Periods:  0
#> Estimation Method:  Doubly Robust

Specify comparison units: 

out3 <- att_gt(yname="lemp",
               tname="year",
               idname="countyreal",
               gname="first.treat",
               xformla=~lpop,
               control_group = "notyettreated",
               data=mpdta)
summary(out3)
#> 
#> Call:
#> att_gt(yname = "lemp", tname = "year", idname = "countyreal", 
#>     gname = "first.treat", xformla = ~lpop, data = mpdta, control_group = "notyettreated")
#> 
#> Reference: Callaway, Brantly and Pedro H.C. Sant'Anna.  "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015> 
#> 
#> Group-Time Average Treatment Effects:
#>  Group Time ATT(g,t) Std. Error [95% Simult.  Conf. Band]  
#>   2004 2004  -0.0212     0.0225       -0.0810      0.0386  
#>   2004 2005  -0.0816     0.0296       -0.1603     -0.0029 *
#>   2004 2006  -0.1382     0.0382       -0.2397     -0.0367 *
#>   2004 2007  -0.1069     0.0353       -0.2007     -0.0131 *
#>   2006 2004  -0.0075     0.0236       -0.0703      0.0553  
#>   2006 2005  -0.0046     0.0193       -0.0559      0.0468  
#>   2006 2006   0.0087     0.0171       -0.0367      0.0540  
#>   2006 2007  -0.0413     0.0195       -0.0931      0.0105  
#>   2007 2004   0.0269     0.0136       -0.0093      0.0632  
#>   2007 2005  -0.0042     0.0158       -0.0461      0.0377  
#>   2007 2006  -0.0284     0.0185       -0.0777      0.0208  
#>   2007 2007  -0.0288     0.0167       -0.0732      0.0156  
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#> 
#> P-value for pre-test of parallel trends assumption:  0.23326
#> Control Group:  Not Yet Treated,  Anticipation Periods:  0
#> Estimation Method:  Doubly Robust

References

Callaway, Brantly and Pedro H.C. Sant'Anna. \"Difference-in-Differences with Multiple Time Periods.\" Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. doi:10.1016/j.jeconom.2020.12.001, https://arxiv.org/abs/1803.09015

build_sim_dataset

Description

A function for building simulated data

Usage

build_sim_dataset(sp_list, panel = TRUE)

Arguments

sp_list

A list of simulation parameters. See reset.sim to generate some default values for parameters

panel

whether to construct panel data (the default) or repeated cross sections data

Value

a data.frame with the following columns

citation

Description

print the citation for the relevant paper

Usage

citation()

Compute Aggregated Treatment Effect Parameters

Description

Does the heavy lifting on computing aggregated group-time average treatment effects

Usage

compute.aggte(
  MP,
  type = "group",
  balance_e = NULL,
  min_e = -Inf,
  max_e = Inf,
  na.rm = FALSE,
  bstrap = NULL,
  biters = NULL,
  cband = NULL,
  alp = NULL,
  clustervars = NULL,
  call = NULL
)

Arguments

MP

an MP object (i.e., the results of the att_gt() method)

type

Which type of aggregated treatment effect parameter to compute. One option is "simple" (this just computes a weighted average of all group-time average treatment effects with weights proportional to group size). Other options are "dynamic" (this computes average effects across different lengths of exposure to the treatment and is similar to an "event study"; here the overall effect averages the effect of the treatment across all positive lengths of exposure); "group" (this is the default option and computes average treatment effects across different groups; here the overall effect averages the effect across different groups); and "calendar" (this computes average treatment effects across different time periods; here the overall effect averages the effect across each time period).

balance_e

If set (and if one computes dynamic effects), it balances the sample with respect to event time. For example, if balance.e=2, aggte will drop groups that are not exposed to treatment for at least three periods. (the initial period when e=0 as well as the next two periods when e=1 and the e=2). This ensures that the composition of groups does not change when event time changes.

min_e

For event studies, this is the smallest event time to compute dynamic effects for. By default, min_e = -Inf so that effects at all lengths of exposure are computed.

max_e

For event studies, this is the largest event time to compute dynamic effects for. By default, max_e = Inf so that effects at all lengths of exposure are computed.

na.rm

Logical value if we are to remove missing Values from analyses. Defaults is FALSE.

bstrap

Boolean for whether or not to compute standard errors using the multiplier bootstrap. If standard errors are clustered, then one must set bstrap=TRUE. Default is TRUE (in addition, cband is also by default TRUE indicating that uniform confidence bands will be returned. If bstrap is FALSE, then analytical standard errors are reported.

biters

The number of bootstrap iterations to use. The default is 1000, and this is only applicable if bstrap=TRUE.

cband

Boolean for whether or not to compute a uniform confidence band that covers all of the group-time average treatment effects with fixed probability 1-alp. In order to compute uniform confidence bands, bstrap must also be set to TRUE. The default is TRUE.

alp

the significance level, default is 0.05

clustervars

A vector of variables names to cluster on. At most, there can be two variables (otherwise will throw an error) and one of these must be the same as idname which allows for clustering at the individual level. By default, we cluster at individual level (when bstrap=TRUE).

call

The function call to aggte

Value

AGGTEobj object

Compute Group-Time Average Treatment Effects

Description

compute.att_gt does the main work for computing multiperiod group-time average treatment effects

Usage

compute.att_gt(dp)

Arguments

dp

A DIDparams object

Value

a list with length equal to the number of groups times the number of time periods; each element of the list contains an object that contains group-time average treatment effect as well as which group it is for and which time period it is for. It also exports the influence function which is used externally to compute standard errors.

Pre-Test of Conditional Parallel Trends Assumption

Description

An integrated moments test for the conditional parallel trends assumption holding in all pre-treatment time periods for all groups

Usage

conditional_did_pretest(
  yname,
  tname,
  idname = NULL,
  gname,
  xformla = NULL,
  data,
  panel = TRUE,
  allow_unbalanced_panel = FALSE,
  control_group = c("nevertreated", "notyettreated"),
  weightsname = NULL,
  alp = 0.05,
  bstrap = TRUE,
  cband = TRUE,
  biters = 1000,
  clustervars = NULL,
  est_method = "ipw",
  print_details = FALSE,
  pl = FALSE,
  cores = 1
)

Arguments

yname

The name of the outcome variable

tname

The name of the column containing the time periods

idname

The individual (cross-sectional unit) id name

gname

The name of the variable in data that contains the first period when a particular observation is treated. This should be a positive number for all observations in treated groups. It defines which "group" a unit belongs to. It should be 0 for units in the untreated group.

xformla

A formula for the covariates to include in the model. It should be of the form ~ X1 + X2. Default is NULL which is equivalent to xformla=~1. This is used to create a matrix of covariates which is then passed to the 2x2 DID estimator chosen in est_method.

data

The name of the data.frame that contains the data

panel

Whether or not the data is a panel dataset. The panel dataset should be provided in long format – that is, where each row corresponds to a unit observed at a particular point in time. The default is TRUE. When is using a panel dataset, the variable idname must be set. When panel=FALSE, the data is treated as repeated cross sections.

allow_unbalanced_panel

Whether or not function should "balance" the panel with respect to time and id. The default values if FALSE which means that att_gt() will drop all units where data is not observed in all periods. The advantage of this is that the computations are faster (sometimes substantially).

control_group

Which units to use the control group. The default is "nevertreated" which sets the control group to be the group of units that never participate in the treatment. This group does not change across groups or time periods. The other option is to set group="notyettreated". In this case, the control group is set to the group of units that have not yet participated in the treatment in that time period. This includes all never treated units, but it includes additional units that eventually participate in the treatment, but have not participated yet.

weightsname

The name of the column containing the sampling weights. If not set, all observations have same weight.

alp

the significance level, default is 0.05

bstrap

Boolean for whether or not to compute standard errors using the multiplier bootstrap. If standard errors are clustered, then one must set bstrap=TRUE. Default is TRUE (in addition, cband is also by default TRUE indicating that uniform confidence bands will be returned. If bstrap is FALSE, then analytical standard errors are reported.

cband

Boolean for whether or not to compute a uniform confidence band that covers all of the group-time average treatment effects with fixed probability 1-alp. In order to compute uniform confidence bands, bstrap must also be set to TRUE. The default is TRUE.

biters

The number of bootstrap iterations to use. The default is 1000, and this is only applicable if bstrap=TRUE.

clustervars

A vector of variables names to cluster on. At most, there can be two variables (otherwise will throw an error) and one of these must be the same as idname which allows for clustering at the individual level. By default, we cluster at individual level (when bstrap=TRUE).

est_method

the method to compute group-time average treatment effects. The default is "dr" which uses the doubly robust approach in the DRDID package. Other built-in methods include "ipw" for inverse probability weighting and "reg" for first step regression estimators. The user can also pass their own function for estimating group time average treatment effects. This should be a function f(Y1,Y0,treat,covariates) where Y1 is an n x 1 vector of outcomes in the post-treatment outcomes, Y0 is an n x 1 vector of pre-treatment outcomes, treat is a vector indicating whether or not an individual participates in the treatment, and covariates is an n x k matrix of covariates. The function should return a list that includes ATT (an estimated average treatment effect), and inf.func (an n x 1 influence function). The function can return other things as well, but these are the only two that are required. est_method is only used if covariates are included.

print_details

Whether or not to show details/progress of computations. Default is FALSE.

pl

Whether or not to use parallel processing

cores

The number of cores to use for parallel processing

Value

an MP.TEST object

References

Callaway, Brantly and Sant'Anna, Pedro H. C. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment." Working Paper https://arxiv.org/abs/1803.09015v2 (2018).

Examples

## Not run: 
data(mpdta)
pre.test <- conditional_did_pretest(yname="lemp",
                                    tname="year",
                                    idname="countyreal",
                                    gname="first.treat",
                                    xformla=~lpop,
                                    data=mpdta)
summary(pre.test)

## End(Not run)

Difference in Differences

Description

Difference in Differences with multiple periods and variation in treatment timing

Take influence function and return standard errors

Description

Function to take an nx1 influence function and return a standard error

Usage

getSE(thisinffunc, DIDparams = NULL)

Arguments

thisinffunc

An influence function

Value

scalar standard error

Get an influence function for particular aggregate parameters

Description

Get an influence function for particular aggregate parameters

This is a generic internal function for combining influence functions across ATT(g,t)'s to return an influence function for various aggregated treatment effect parameters.

Usage

get_agg_inf_func(att, inffunc1, whichones, weights.agg, wif = NULL)

Arguments

att

vector of group-time average treatment effects

inffunc1

influence function for all group-time average treatment effects (matrix)

whichones

which elements of att will be used to compute the aggregated treatment effect parameter

weights.agg

the weights to apply to each element of att(whichones); should have the same dimension as att(whichones)

wif

extra influence function term coming from estimating the weights; should be n x k matrix where k is dimension of whichones

Value

nx1 influence function

Plot did objects using ggplot2

Description

Function to plot objects from the did package

Usage

ggdid(object, ...)

Arguments

object

either a MP object or AGGTEobj object. See help(ggdid.MP) and help(ggdid.AGGTEobj).

...

other arguments

Plot AGGTEobj objects

Description

A function to plot AGGTEobj objects

Usage

## S3 method for class 'AGGTEobj'
ggdid(
  object,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  title = "",
  xgap = 1,
  legend = TRUE,
  ref_line = 0,
  theming = TRUE,
  ...
)

Arguments

object

either a MP object or AGGTEobj object. See help(ggdid.MP) and help(ggdid.AGGTEobj).

ylim

optional y limits for the plot; setting here makes the y limits the same across different plots

xlab

optional x-axis label

ylab

optional y-axis label

title

optional plot title

xgap

optional gap between the labels on the x-axis. For example, xgap=3 indicates that the labels should show up for every third value on the x-axis. The default is 1.

legend

Whether or not to include a legend (which will indicate color of pre- and post-treatment estimates). Default is TRUE.

ref_line

A reference line at this value, usually to compare confidence intervals to 0. Set to NULL to omit.

theming

Set to FALSE to skip all theming so you can do it yourself.

...

other arguments

Plot MP objects using ggplot2

Description

A function to plot MP objects

Usage

## S3 method for class 'MP'
ggdid(
  object,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  title = "Group",
  xgap = 1,
  ncol = 1,
  legend = TRUE,
  group = NULL,
  ref_line = 0,
  theming = TRUE,
  grtitle = "Group",
  ...
)

Arguments

object

either a MP object or AGGTEobj object. See help(ggdid.MP) and help(ggdid.AGGTEobj).

ylim

optional y limits for the plot; setting here makes the y limits the same across different plots

xlab

optional x-axis label

ylab

optional y-axis label

title

optional plot title

xgap

optional gap between the labels on the x-axis. For example, xgap=3 indicates that the labels should show up for every third value on the x-axis. The default is 1.

ncol

The number of columns to include in the resulting plot. The default is 1.

legend

Whether or not to include a legend (which will indicate color of pre- and post-treatment estimates). Default is TRUE.

group

Vector for which groups to include in the plots of ATT(g,t). Default is NULL, and, in this case, plots for all groups will be included (ggdid.MP only).

ref_line

A reference line at this value, usually to compare confidence intervals to 0. Set to NULL to omit.

theming

Set to FALSE to skip all theming so you can do it yourself.

grtitle

Title to append before each group name (ggdid.MP only).

...

other arguments

glance model characteristics from AGGTEobj objects

Description

glance model characteristics from AGGTEobj objects

Usage

## S3 method for class 'AGGTEobj'
glance(x, ...)

Arguments

x

a model of class AGGTEobj produced by the aggte() function

...

other arguments passed to methods

glance model characteristics from MP objects

Description

glance model characteristics from MP objects

Usage

## S3 method for class 'MP'
glance(x, ...)

Arguments

x

a model of class MP produced by the att_gt() function

...

other arguments passed to methods

gplot

Description

does the heavy lifting for making a plot of an group-time average treatment effect

Usage

gplot(
  ssresults,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  title = "Group",
  xgap = 1,
  legend = TRUE,
  ref_line = 0,
  theming = TRUE
)

Value

a ggplot2 object

indicator

Description

indicator weighting function

Usage

indicator(X, u)

Arguments

X

matrix of X's from the data

u

a particular value to compare X's to

Value

numeric vector

Examples

data(mpdta)
dta <- subset(mpdta, year==2007)
X <- model.matrix(~lpop, data=dta)
X <- indicator(X, X[1,])

Multiplier Bootstrap

Description

A function to take an influence function and use the multiplier bootstrap to compute standard errors and critical values for uniform confidence bands.

Usage

mboot(inf.func, DIDparams, pl = FALSE, cores = 1)

Arguments

inf.func

an influence function

DIDparams

DIDparams object

pl

whether or not to use parallel processing in the multiplier bootstrap, default=FALSE

cores

the number of cores to use with parallel processing, default=1

Value

list with elements

bres

results from each bootstrap iteration

V

variance matrix

se

standard errors

crit.val

a critical value for computing uniform confidence bands

County Teen Employment Dataset

Description

A dataset containing (the log of) teen employment in 500 counties in the U.S. from 2004 to 2007. This is a subset of the dataset used in Callaway and Sant'Anna (2021). See that paper for additional descriptions.

Usage

mpdta

Format

A data frame with 2000 rows and 5 variables:

year

the year of the observation

countyreal

a unique identifier for a particular county

lpop

the log of 1000s of population for the county

lemp

the log of teen employment in the county

first.treat

the year that the state where the county is located raised its minimum wage, it is set equal to 0 for counties that have minimum wages equal to the federal minimum wage over the entire period.

treat

whether or not a particular county is treated in that year

Source

Callaway and Sant'Anna (2020)

Process did Function Arguments

Description

Function to process arguments passed to the main methods in the did package as well as conducting some tests to make sure data is in proper format / try to throw helpful error messages.

Usage

pre_process_did(
  yname,
  tname,
  idname,
  gname,
  xformla = NULL,
  data,
  panel = TRUE,
  allow_unbalanced_panel,
  control_group = c("nevertreated", "notyettreated"),
  anticipation = 0,
  weightsname = NULL,
  alp = 0.05,
  bstrap = FALSE,
  cband = FALSE,
  biters = 1000,
  clustervars = NULL,
  est_method = "dr",
  base_period = "varying",
  print_details = TRUE,
  pl = FALSE,
  cores = 1,
  call = NULL
)

Arguments

yname

The name of the outcome variable

tname

The name of the column containing the time periods

idname

The individual (cross-sectional unit) id name

gname

The name of the variable in data that contains the first period when a particular observation is treated. This should be a positive number for all observations in treated groups. It defines which "group" a unit belongs to. It should be 0 for units in the untreated group.

xformla

A formula for the covariates to include in the model. It should be of the form ~ X1 + X2. Default is NULL which is equivalent to xformla=~1. This is used to create a matrix of covariates which is then passed to the 2x2 DID estimator chosen in est_method.

data

The name of the data.frame that contains the data

panel

Whether or not the data is a panel dataset. The panel dataset should be provided in long format – that is, where each row corresponds to a unit observed at a particular point in time. The default is TRUE. When is using a panel dataset, the variable idname must be set. When panel=FALSE, the data is treated as repeated cross sections.

allow_unbalanced_panel

Whether or not function should "balance" the panel with respect to time and id. The default values if FALSE which means that att_gt() will drop all units where data is not observed in all periods. The advantage of this is that the computations are faster (sometimes substantially).

control_group

Which units to use the control group. The default is "nevertreated" which sets the control group to be the group of units that never participate in the treatment. This group does not change across groups or time periods. The other option is to set group="notyettreated". In this case, the control group is set to the group of units that have not yet participated in the treatment in that time period. This includes all never treated units, but it includes additional units that eventually participate in the treatment, but have not participated yet.

anticipation

The number of time periods before participating in the treatment where units can anticipate participating in the treatment and therefore it can affect their untreated potential outcomes

weightsname

The name of the column containing the sampling weights. If not set, all observations have same weight.

alp

the significance level, default is 0.05

bstrap

Boolean for whether or not to compute standard errors using the multiplier bootstrap. If standard errors are clustered, then one must set bstrap=TRUE. Default is TRUE (in addition, cband is also by default TRUE indicating that uniform confidence bands will be returned. If bstrap is FALSE, then analytical standard errors are reported.

cband

Boolean for whether or not to compute a uniform confidence band that covers all of the group-time average treatment effects with fixed probability 1-alp. In order to compute uniform confidence bands, bstrap must also be set to TRUE. The default is TRUE.

biters

The number of bootstrap iterations to use. The default is 1000, and this is only applicable if bstrap=TRUE.

clustervars

A vector of variables names to cluster on. At most, there can be two variables (otherwise will throw an error) and one of these must be the same as idname which allows for clustering at the individual level. By default, we cluster at individual level (when bstrap=TRUE).

est_method

the method to compute group-time average treatment effects. The default is "dr" which uses the doubly robust approach in the DRDID package. Other built-in methods include "ipw" for inverse probability weighting and "reg" for first step regression estimators. The user can also pass their own function for estimating group time average treatment effects. This should be a function f(Y1,Y0,treat,covariates) where Y1 is an n x 1 vector of outcomes in the post-treatment outcomes, Y0 is an n x 1 vector of pre-treatment outcomes, treat is a vector indicating whether or not an individual participates in the treatment, and covariates is an n x k matrix of covariates. The function should return a list that includes ATT (an estimated average treatment effect), and inf.func (an n x 1 influence function). The function can return other things as well, but these are the only two that are required. est_method is only used if covariates are included.

base_period

Whether to use a "varying" base period or a "universal" base period. Either choice results in the same post-treatment estimates of ATT(g,t)'s. In pre-treatment periods, using a varying base period amounts to computing a pseudo-ATT in each treatment period by comparing the change in outcomes for a particular group relative to its comparison group in the pre-treatment periods (i.e., in pre-treatment periods this setting computes changes from period t-1 to period t, but repeatedly changes the value of t)

A universal base period fixes the base period to always be (g-anticipation-1). This does not compute pseudo-ATT(g,t)'s in pre-treatment periods, but rather reports average changes in outcomes from period t to (g-anticipation-1) for a particular group relative to its comparison group. This is analogous to what is often reported in event study regressions.

Using a varying base period results in an estimate of ATT(g,t) being reported in the period immediately before treatment. Using a universal base period normalizes the estimate in the period right before treatment (or earlier when the user allows for anticipation) to be equal to 0, but one extra estimate in an earlier period.

print_details

Whether or not to show details/progress of computations. Default is FALSE.

pl

Whether or not to use parallel processing

cores

The number of cores to use for parallel processing

call

Function call to att_gt

Value

a DIDparams object

print.AGGTEobj

Description

prints value of a AGGTEobj object

Usage

## S3 method for class 'AGGTEobj'
print(x, ...)

Arguments

x

a AGGTEobj object

...

extra arguments

print.MP

Description

prints value of a MP object

Usage

## S3 method for class 'MP'
print(x, ...)

Arguments

x

a MP object

...

extra arguments

Process Results from compute.att_gt()

Description

Process Results from compute.att_gt()

Usage

process_attgt(attgt.list)

Arguments

attgt.list

list of results from compute.att_gt()

Value

list with elements:

group

which group a set of results belongs to

tt

which time period a set of results belongs to

att

the group time average treatment effect

tidy results

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

glance, tidy

reset.sim

Description

a function to create a "reasonable" set of parameters to create simulated panel data that obeys a parallel trends assumption. In particular, it provides parameters where the the effect of participating in the treatment is equal to one in all post-treatment time periods.

After calling this function, the user can change particular values of the parameters in order to generate dynamics, heterogeneous effects across groups, etc.

Usage

reset.sim(time.periods = 4, n = 5000, ipw = TRUE, reg = TRUE)

Arguments

time.periods

The number of time periods to include

n

The total number of observations

ipw

If TRUE, sets parameters so that DGP is compatible with recovering ATT(g,t)'s using IPW (i.e., where logit that just includes a linear term in X works). If FALSE, sets parameters that will be incompatible with IPW. Either way, these parameters can be specified by the user if so desired.

reg

If TRUE, sets parameters so that DGP is compatible with recovering ATT(g,t)'s using regressions on untreated untreated potential outcomes. If FALSE, sets parameters that will be incompatible with using regressions (i.e., regressions that include only linear term in X). Either way, these parameters can be specified by the user if so desired.

Value

list of simulation parameters

sim

Description

An internal function that builds simulated data, computes ATT(g,t)'s and some aggregations. It is useful for testing the inference procedures in the did function.

Usage

sim(
  sp_list,
  ret = NULL,
  bstrap = TRUE,
  cband = TRUE,
  control_group = "nevertreated",
  xformla = ~X,
  est_method = "dr",
  clustervars = NULL,
  panel = TRUE
)

Arguments

sp_list

A list of simulation parameters. See reset.sim to generate some default values for parameters

ret

which type of results to return. The options are Wpval (returns 1 if the p-value from a Wald test that all pre-treatment ATT(g,t)'s are equal is less than .05), cband (returns 1 if a uniform confidence band covers 0 for groups and times), simple (returns 1 if, using the simple treatment effect aggregation results in rejecting that this aggregated treatment effect parameter is equal to 0), dynamic (returns 1 if the uniform confidence band from the dynamic treatment effect aggregation covers 0 in all pre- and post-treatment periods). The default value is NULL, and in this case the function will just return the results from the call to att_gt.

bstrap

whether or not to use the bootstrap to conduct inference (default is TRUE)

cband

whether or not to compute uniform confidence bands in the call to att_gt (the default is TRUE)

control_group

Whether to use the "nevertreated" comparison group (the default) or the "notyettreated" as the comparison group

xformla

Formula for covariates in att_gt (default is ~X)

est_method

Which estimation method to use in att_gt (default is "dr")

clustervars

Any additional variables which should be clustered on

panel

whether to simulate panel data (the default) or otherwise repeated cross sections data

Value

When ret=NULL, returns the results of the call to att_gt, otherwise returns 1 if the specified test rejects or 0 if not.

splot

Description

alternative plotting function for the case with selective treatment timing

Usage

splot(
  ssresults,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  title = "Group",
  legend = TRUE,
  ref_line = 0,
  theming = TRUE
)

Value

a ggplot2 object

Summary Aggregate Treatment Effect Parameter Objects

Description

A function to summarize aggregated treatment effect parameters.

Usage

## S3 method for class 'AGGTEobj'
summary(object, ...)

Arguments

object

an AGGTEobj object

...

other arguments

summary.MP

Description

prints a summary of a MP object

Usage

## S3 method for class 'MP'
summary(object, ...)

Arguments

object

an MP object

...

extra arguments

summary.MP.TEST

Description

print a summary of test results

Usage

## S3 method for class 'MP.TEST'
summary(object, ...)

Arguments

object

an MP.TEST object

...

other variables

Multiplier Bootstrap for Conditional Moment Test

Description

A slightly modified multiplier bootstrap procedure for the pre-test of the conditional parallel trends assumption

Usage

test.mboot(inf.func, DIDparams, cores = 1)

Arguments

inf.func

an influence function

DIDparams

DIDparams object

cores

The number of cores to use to bootstrap the test statistic in parallel. Default is cores=1 which corresponds to not running parallel.

Value

list

bres

CvM test statistics for each bootstrap iteration

crit.val

critical value for CvM test statistic

tidy results from AGGTEobj objects

Description

tidy results from AGGTEobj objects

Usage

## S3 method for class 'AGGTEobj'
tidy(x, ...)

Arguments

x

a model of class AGGTEobj produced by the aggte() function

...

Additional arguments to tidying method.

tidy results from MP objects

Description

tidy results from MP objects

Usage

## S3 method for class 'MP'
tidy(x, ...)

Arguments

x

a model of class MP produced by the att_gt() function

...

Additional arguments to tidying method.

trimmer

Description

A utility function to find observations that appear to violate support conditions. This function is not called anywhere in the code, but it is just useful for debugging some common issues that users run into.

Usage

trimmer(
  g,
  tname,
  idname,
  gname,
  xformla,
  data,
  control_group = "notyettreated",
  threshold = 0.999
)

Arguments

g

is a particular group (below I pass in 2009)

tname

The name of the column containing the time periods

idname

The individual (cross-sectional unit) id name

gname

The name of the variable in data that contains the first period when a particular observation is treated. This should be a positive number for all observations in treated groups. It defines which "group" a unit belongs to. It should be 0 for units in the untreated group.

xformla

A formula for the covariates to include in the model. It should be of the form ~ X1 + X2. Default is NULL which is equivalent to xformla=~1. This is used to create a matrix of covariates which is then passed to the 2x2 DID estimator chosen in est_method.

data

The name of the data.frame that contains the data

control_group

Which units to use the control group. The default is "nevertreated" which sets the control group to be the group of units that never participate in the treatment. This group does not change across groups or time periods. The other option is to set group="notyettreated". In this case, the control group is set to the group of units that have not yet participated in the treatment in that time period. This includes all never treated units, but it includes additional units that eventually participate in the treatment, but have not participated yet.

threshold

the cutoff for which observations are flagged as likely violators of the support condition.

Value

list of ids of observations that likely violate support conditions

Compute extra term in influence function due to estimating weights

Description

A function to compute the extra term that shows up in the influence function for aggregated treatment effect parameters due to estimating the weights

Usage

wif(keepers, pg, weights.ind, G, group)

Arguments

keepers

a vector of indices for which group-time average treatment effects are used to compute a particular aggregated parameter

pg

a vector with same length as total number of group-time average treatment effects that contains the probability of being in particular group

weights.ind

additional sampling weights (nx1)

G

vector containing which group a unit belongs to (nx1)

group

vector of groups

Value

nxk influence function matrix