att_gt: Group-Time Average Treatment Effects
In did: Treatment Effects with Multiple Periods and Groups
att_gt R Documentation
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,
fix_weights = NULL,
alp = 0.05,
bstrap = TRUE,
cband = TRUE,
biters = 1000,
clustervars = NULL,
est_method = "dr",
base_period = "varying",
faster_mode = TRUE,
print_details = FALSE,
pl = FALSE,
cores = 1,
compute_inffunc = TRUE,
...
)
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.
For time-varying covariates: (1) With balanced panel data,
in each 2x2 comparison, the covariates
are taken to be the value of the covariates in the earlier time
period, and all of the underlying computations involve changes in Y
as a function of those values of covariates. (2) With repeated cross
sections data and unbalanced panel data, the covariates are taken
from each time period and computations involve Y_post conditional
on X_post minus Y_pre conditional on X_pre. A byproduct of this
is that, with balanced panel data and in the presence of
time-varying covariates, it is possible to get different numerical
results according to whether or not allow_unbalanced_panel=TRUE or
FALSE.
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
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
value is 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 as 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. When weights are
time-invariant (constant within each unit across periods), all
fix_weights options produce identical results and no special
handling is needed.
When weights vary across time (e.g., time-varying population sizes),
the default behavior differs by panel type:
- Balanced panel
Each 2x2 DiD comparison uses the weight from the
earlier of the two time periods involved. For post-treatment cells,
this is the base period (g-1). For pre-treatment cells with
base_period="varying", this is the pre-treatment period itself.
The panel DRDID estimators are used.
- Repeated cross sections and unbalanced panels
Both periods'
per-observation weights are passed directly to the RC DRDID estimators,
so each observation carries its own period-specific weight.
Use the fix_weights argument to override the default behavior.
fix_weights
Controls how time-varying sampling weights are resolved.
Only relevant when weights vary across time; with time-invariant weights,
all options produce identical results. Options:
NULL (default)For balanced panel: uses the weight from
the earlier of the two time periods in each 2x2 comparison. For
post-treatment cells, this is the base period (g-1). For
pre-treatment cells, this depends on the base_period setting.
For RC/unbalanced panel: uses per-observation weights from both
periods.
"varying"Uses per-observation, period-specific weights
for all panel types. For balanced panel data, this switches to the
repeated cross-section DRDID estimators so that pre-period and
post-period observations each carry their own weight. Covariates
are held fixed at their pre-period values (same as the default
panel estimator). This is the most flexible option for weights but
sacrifices the efficiency of the panel estimator. For RC/unbalanced
panel, this is identical to the default. Not supported with custom
est_method functions.
"base_period"Fixes weights at the base period (g-1) for
all (g,t) cells within a group, for both pre-treatment and
post-treatment comparisons. Ensures all cells within a group use the
same weights. For unbalanced panels, units not observed in the base
period are dropped with a warning. Not supported for repeated cross
sections (panel = FALSE).
"first_period"Fixes weights at the first time period in
the dataset for all (g,t) cells. For unbalanced panels, units not
observed in the first period are dropped with a warning. Not supported
for repeated cross sections (panel = FALSE).
alp
the significance level, default is 0.05
bstrap
Boolean for whether or not to compute standard errors using
the multiplier bootstrap. Default is TRUE (in addition, cband
is also by default TRUE indicating that uniform confidence bands
will be returned). If bstrap=FALSE, analytical standard errors are
reported; these are cluster-robust when clustervars is supplied.
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. Clustered standard errors are available with the multiplier bootstrap
(bstrap=TRUE) or analytically (bstrap=FALSE).
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. The required signature depends on the data structure:
Panel data (panel=TRUE): f(y1, y0, D, covariates, i.weights, inffunc, ...) where y1 is an n x 1 vector of
post-treatment outcomes, y0 is an n x 1 vector of
pre-treatment outcomes, D is a binary vector indicating
treatment group membership, covariates is an n x k matrix,
i.weights is a vector of sampling weights, and inffunc is a
logical requesting influence-function computation.
Repeated cross sections / unbalanced panel (panel=FALSE):
f(y, post, D, covariates, i.weights, inffunc, ...) where y is
the outcome vector (length n), post is a binary indicator for
the post-treatment period, D is a binary treatment indicator,
covariates is an n x k matrix, i.weights is a vector of
sampling weights, and inffunc is a logical.
In both cases the function should return a list that includes
ATT (the estimated group-time average treatment effect) and
att.inf.func (an n x 1 influence function — one entry per
observation passed into the estimator).
The function can return other things as well, but these are
the only two that are required. With no covariates
(xformla = NULL), the built-in methods ("dr", "ipw", "reg") all
reduce to the unconditional difference-in-differences estimator, so
the choice among them is irrelevant; a custom est_method function
is still called (with an intercept-only covariates matrix) and
determines the estimates.
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.
faster_mode
This option enables a faster version of did, optimizing
computation time for large datasets by improving data management within the package.
The default is set to TRUE. Both modes produce identical results up to
numerical precision; while the difference is minimal for small datasets,
the speedup is substantial for large ones.
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
compute_inffunc
Whether or not to compute the influence functions. The
default is TRUE. The influence functions are required for standard errors,
uniform confidence bands, the parallel-trends pre-test, and for aggregating the
group-time effects with aggte(). Set compute_inffunc = FALSE to obtain the
group-time ATT point estimates only: this is faster and uses substantially less
memory (no n \times k influence-function matrix is formed or bootstrapped),
but the returned object has no standard errors / pre-test and cannot be passed to
aggte(). The point estimates are identical to those from a full run. When
compute_inffunc = FALSE, bstrap and cband are set to FALSE automatically.
...
Additional arguments to be passed to a custom est_method
function. These are ignored when using built-in estimation methods
("dr", "ipw", "reg").
Value
an MP object containing all the results for group-time average
treatment effects
The returned inffunc matrix collects the estimated influence functions,
with one column per ATT(g,t) and one row per cross-sectional unit (one row
per observation with repeated cross sections). Its rownames hold the unit
ids (idname; an internal observation index for repeated cross sections)
and are the authoritative link between rows and units: the row ORDER is
mode-specific (faster_mode = FALSE sorts units by id, while
faster_mode = TRUE uses an internal (period, cohort, id) ordering), so
external consumers of the influence functions must align rows by rowname,
never by position.
Options
When faster_mode = FALSE, setting options(did.disable_precompute = TRUE)
disables the one-time positional precompute and assembles every 2x2
comparison from the long data per cell, as in earlier versions. The
covariate design matrix is still built once over the full sample under
both settings, so results are identical either way; the option exists
only as a debugging escape hatch.
Examples:
Basic att_gt() call:
# Example data
data(mpdta)
set.seed(09152024)
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 0.0258 -0.0809 0.0599
#> 2004 2005 -0.0704 0.0341 -0.1635 0.0227
#> 2004 2006 -0.1373 0.0384 -0.2423 -0.0322 *
#> 2004 2007 -0.1008 0.0354 -0.1976 -0.0040 *
#> 2006 2004 0.0065 0.0235 -0.0578 0.0708
#> 2006 2005 -0.0028 0.0192 -0.0554 0.0499
#> 2006 2006 -0.0046 0.0184 -0.0548 0.0456
#> 2006 2007 -0.0412 0.0207 -0.0977 0.0153
#> 2007 2004 0.0305 0.0161 -0.0135 0.0746
#> 2007 2005 -0.0027 0.0157 -0.0456 0.0401
#> 2007 2006 -0.0311 0.0184 -0.0815 0.0193
#> 2007 2007 -0.0261 0.0176 -0.0741 0.0220
#> ---
#> 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 0.0222 -0.0737 0.0446
#> 2004 2005 -0.0764 0.0303 -0.1570 0.0041
#> 2004 2006 -0.1404 0.0382 -0.2421 -0.0388 *
#> 2004 2007 -0.1069 0.0358 -0.2021 -0.0117 *
#> 2006 2004 -0.0005 0.0231 -0.0618 0.0609
#> 2006 2005 -0.0062 0.0188 -0.0561 0.0437
#> 2006 2006 0.0010 0.0204 -0.0534 0.0553
#> 2006 2007 -0.0413 0.0210 -0.0971 0.0145
#> 2007 2004 0.0267 0.0140 -0.0104 0.0639
#> 2007 2005 -0.0046 0.0170 -0.0498 0.0407
#> 2007 2006 -0.0284 0.0187 -0.0782 0.0213
#> 2007 2007 -0.0288 0.0161 -0.0715 0.0140
#> ---
#> 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 0.0217 -0.0788 0.0365
#> 2004 2005 -0.0816 0.0324 -0.1676 0.0044
#> 2004 2006 -0.1382 0.0368 -0.2361 -0.0403 *
#> 2004 2007 -0.1069 0.0344 -0.1984 -0.0154 *
#> 2006 2004 -0.0075 0.0233 -0.0693 0.0544
#> 2006 2005 -0.0046 0.0184 -0.0533 0.0442
#> 2006 2006 0.0087 0.0182 -0.0397 0.0570
#> 2006 2007 -0.0413 0.0205 -0.0956 0.0130
#> 2007 2004 0.0269 0.0136 -0.0091 0.0630
#> 2007 2005 -0.0042 0.0153 -0.0448 0.0364
#> 2007 2006 -0.0284 0.0191 -0.0792 0.0223
#> 2007 2007 -0.0288 0.0167 -0.0732 0.0157
#> ---
#> 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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jeconom.2020.12.001")}, https://arxiv.org/abs/1803.09015
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| att_gt | R Documentation |
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,
fix_weights = NULL,
alp = 0.05,
bstrap = TRUE,
cband = TRUE,
biters = 1000,
clustervars = NULL,
est_method = "dr",
base_period = "varying",
faster_mode = TRUE,
print_details = FALSE,
pl = FALSE,
cores = 1,
compute_inffunc = TRUE,
...
)
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 |
xformla |
A formula for the covariates to include in the
model. It should be of the form For time-varying covariates: (1) With balanced panel data,
in each 2x2 comparison, the covariates
are taken to be the value of the covariates in the earlier time
period, and all of the underlying computations involve changes in Y
as a function of those values of covariates. (2) With repeated cross
sections data and unbalanced panel data, the covariates are taken
from each time period and computations involve Y_post conditional
on X_post minus Y_pre conditional on X_pre. A byproduct of this
is that, with balanced panel data and in the presence of
time-varying covariates, it is possible to get different numerical
results according to whether or not |
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
using a panel dataset, the variable |
allow_unbalanced_panel |
Whether or not function should
"balance" the panel with respect to time and id. The default
value is |
control_group |
Which units to use as 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
|
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. When weights are
time-invariant (constant within each unit across periods), all
When weights vary across time (e.g., time-varying population sizes), the default behavior differs by panel type:
Use the |
fix_weights |
Controls how time-varying sampling weights are resolved. Only relevant when weights vary across time; with time-invariant weights, all options produce identical results. Options:
|
alp |
the significance level, default is 0.05 |
bstrap |
Boolean for whether or not to compute standard errors using
the multiplier bootstrap. Default is |
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 |
biters |
The number of bootstrap iterations to use. The default is 1000,
and this is only applicable if |
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. Clustered standard errors are available with the multiplier bootstrap
( |
est_method |
the method to compute group-time average treatment effects. The default is "dr" which uses the doubly robust
approach in the Panel data ( Repeated cross sections / unbalanced panel ( In both cases the function should return a list that includes
|
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. |
faster_mode |
This option enables a faster version of |
print_details |
Whether or not to show details/progress of computations.
Default is |
pl |
Whether or not to use parallel processing |
cores |
The number of cores to use for parallel processing |
compute_inffunc |
Whether or not to compute the influence functions. The
default is |
... |
Additional arguments to be passed to a custom |
Value
an MP object containing all the results for group-time average
treatment effects
The returned inffunc matrix collects the estimated influence functions,
with one column per ATT(g,t) and one row per cross-sectional unit (one row
per observation with repeated cross sections). Its rownames hold the unit
ids (idname; an internal observation index for repeated cross sections)
and are the authoritative link between rows and units: the row ORDER is
mode-specific (faster_mode = FALSE sorts units by id, while
faster_mode = TRUE uses an internal (period, cohort, id) ordering), so
external consumers of the influence functions must align rows by rowname,
never by position.
Options
When faster_mode = FALSE, setting options(did.disable_precompute = TRUE)
disables the one-time positional precompute and assembles every 2x2
comparison from the long data per cell, as in earlier versions. The
covariate design matrix is still built once over the full sample under
both settings, so results are identical either way; the option exists
only as a debugging escape hatch.
Examples:
Basic att_gt() call:
# Example data
data(mpdta)
set.seed(09152024)
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 0.0258 -0.0809 0.0599
#> 2004 2005 -0.0704 0.0341 -0.1635 0.0227
#> 2004 2006 -0.1373 0.0384 -0.2423 -0.0322 *
#> 2004 2007 -0.1008 0.0354 -0.1976 -0.0040 *
#> 2006 2004 0.0065 0.0235 -0.0578 0.0708
#> 2006 2005 -0.0028 0.0192 -0.0554 0.0499
#> 2006 2006 -0.0046 0.0184 -0.0548 0.0456
#> 2006 2007 -0.0412 0.0207 -0.0977 0.0153
#> 2007 2004 0.0305 0.0161 -0.0135 0.0746
#> 2007 2005 -0.0027 0.0157 -0.0456 0.0401
#> 2007 2006 -0.0311 0.0184 -0.0815 0.0193
#> 2007 2007 -0.0261 0.0176 -0.0741 0.0220
#> ---
#> 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 0.0222 -0.0737 0.0446
#> 2004 2005 -0.0764 0.0303 -0.1570 0.0041
#> 2004 2006 -0.1404 0.0382 -0.2421 -0.0388 *
#> 2004 2007 -0.1069 0.0358 -0.2021 -0.0117 *
#> 2006 2004 -0.0005 0.0231 -0.0618 0.0609
#> 2006 2005 -0.0062 0.0188 -0.0561 0.0437
#> 2006 2006 0.0010 0.0204 -0.0534 0.0553
#> 2006 2007 -0.0413 0.0210 -0.0971 0.0145
#> 2007 2004 0.0267 0.0140 -0.0104 0.0639
#> 2007 2005 -0.0046 0.0170 -0.0498 0.0407
#> 2007 2006 -0.0284 0.0187 -0.0782 0.0213
#> 2007 2007 -0.0288 0.0161 -0.0715 0.0140
#> ---
#> 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 0.0217 -0.0788 0.0365
#> 2004 2005 -0.0816 0.0324 -0.1676 0.0044
#> 2004 2006 -0.1382 0.0368 -0.2361 -0.0403 *
#> 2004 2007 -0.1069 0.0344 -0.1984 -0.0154 *
#> 2006 2004 -0.0075 0.0233 -0.0693 0.0544
#> 2006 2005 -0.0046 0.0184 -0.0533 0.0442
#> 2006 2006 0.0087 0.0182 -0.0397 0.0570
#> 2006 2007 -0.0413 0.0205 -0.0956 0.0130
#> 2007 2004 0.0269 0.0136 -0.0091 0.0630
#> 2007 2005 -0.0042 0.0153 -0.0448 0.0364
#> 2007 2006 -0.0284 0.0191 -0.0792 0.0223
#> 2007 2007 -0.0288 0.0167 -0.0732 0.0157
#> ---
#> 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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jeconom.2020.12.001")}, https://arxiv.org/abs/1803.09015
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