`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.

```
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
)
```

- 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

an `MP`

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

**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 0.0249 -0.0772 0.0562
#> 2004 2005 -0.0704 0.0320 -0.1560 0.0152
#> 2004 2006 -0.1373 0.0392 -0.2421 -0.0324 *
#> 2004 2007 -0.1008 0.0363 -0.1980 -0.0037 *
#> 2006 2004 0.0065 0.0233 -0.0560 0.0690
#> 2006 2005 -0.0028 0.0186 -0.0526 0.0471
#> 2006 2006 -0.0046 0.0176 -0.0517 0.0425
#> 2006 2007 -0.0412 0.0194 -0.0931 0.0106
#> 2007 2004 0.0305 0.0149 -0.0095 0.0705
#> 2007 2005 -0.0027 0.0162 -0.0460 0.0405
#> 2007 2006 -0.0311 0.0174 -0.0777 0.0156
#> 2007 2007 -0.0261 0.0163 -0.0698 0.0177
#> ---
#> 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 0.0224 -0.0749 0.0458
#> 2004 2005 -0.0764 0.0300 -0.1573 0.0045
#> 2004 2006 -0.1404 0.0363 -0.2385 -0.0424 *
#> 2004 2007 -0.1069 0.0340 -0.1988 -0.0150 *
#> 2006 2004 -0.0005 0.0231 -0.0629 0.0619
#> 2006 2005 -0.0062 0.0189 -0.0572 0.0448
#> 2006 2006 0.0010 0.0205 -0.0542 0.0562
#> 2006 2007 -0.0413 0.0195 -0.0939 0.0114
#> 2007 2004 0.0267 0.0145 -0.0125 0.0659
#> 2007 2005 -0.0046 0.0165 -0.0491 0.0400
#> 2007 2006 -0.0284 0.0186 -0.0786 0.0217
#> 2007 2007 -0.0288 0.0154 -0.0704 0.0129
#> ---
#> 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 0.0238 -0.0845 0.0422
#> 2004 2005 -0.0816 0.0304 -0.1625 -0.0007 *
#> 2004 2006 -0.1382 0.0379 -0.2391 -0.0373 *
#> 2004 2007 -0.1069 0.0343 -0.1982 -0.0156 *
#> 2006 2004 -0.0075 0.0219 -0.0656 0.0507
#> 2006 2005 -0.0046 0.0199 -0.0575 0.0484
#> 2006 2006 0.0087 0.0165 -0.0351 0.0524
#> 2006 2007 -0.0413 0.0197 -0.0937 0.0111
#> 2007 2004 0.0269 0.0136 -0.0092 0.0630
#> 2007 2005 -0.0042 0.0156 -0.0456 0.0372
#> 2007 2006 -0.0284 0.0185 -0.0776 0.0207
#> 2007 2007 -0.0288 0.0154 -0.0697 0.0121
#> ---
#> 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
```

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