did (version 1.2.3)

mp.spatt: mp.spatt

Description

mp.spatt computes the ATT in the case where there are more than two periods of data and allowing for treatment to occur at different points in time extending the method of Abadie (2005). This method relies on once individuals are treated they remain in the treated state for the duration.

Usage

mp.spatt(
  formla,
  xformla = NULL,
  data,
  tname,
  aggte = TRUE,
  w = NULL,
  panel = FALSE,
  idname = NULL,
  first.treat.name,
  alp = 0.05,
  method = "logit",
  se = TRUE,
  bstrap = FALSE,
  biters = 100,
  clustervars = NULL,
  cband = FALSE,
  citers = 100,
  seedvec = NULL,
  pl = FALSE,
  cores = 2,
  printdetails = TRUE
)

Arguments

formla

The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary)

xformla

A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.

data

The name of the data.frame that contains the data

tname

The name of the column containing the time periods

aggte

boolean for whether or not to compute aggregate treatment effect parameters, default TRUE

w

A vector of weights for each observation (not implemented)

panel

Boolean indicating whether the data is panel or repeated cross sections

idname

The individual (cross-sectional unit) id name

first.treat.name

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 should be 0 for observations in the untreated group.

alp

the significance level, default is 0.05

method

The method for estimating the propensity score when covariates are included

se

Boolean whether or not to compute standard errors

bstrap

Boolean for whether or not to compute standard errors using the multiplier boostrap. If standard errors are clustered, then one must set bstrap=TRUE.

biters

The number of boostrap iterations to use. The default is 100, and this is only applicable if bstrap=TRUE.

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.

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. The default is FALSE and the resulting standard errors will be pointwise.

citers

Computing uniform confidence bands requires the bootstrap, if cband = TRUE, then this is the number of boostrap iterations to compute the conidence band. The default is 100.

seedvec

Optional value to set random seed; can possibly be used in conjunction with bootstrapping standard errors#' (not implemented)

pl

Boolean for whether or not to use parallel processing

cores

The number of cores to use for parallel processing

printdetails

Boolean for showing detailed results or not

Value

MP object

References

Callaway, Brantly and Sant'Anna, Pedro. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment." Working Paper <https://ssrn.com/abstract=3148250> (2018).

Examples

# NOT RUN {
data(mpdta)

## with covariates
out1 <- mp.spatt(lemp ~ treat, xformla=~lpop, data=mpdta,
                panel=TRUE, first.treat.name="first.treat",
                idname="countyreal", tname="year",
                bstrap=FALSE, se=TRUE, cband=FALSE)
## summarize the group-time average treatment effects
summary(out1)
## summarize the aggregated treatment effect parameters
summary(out1$aggte)

## without any covariates
out2 <- mp.spatt(lemp ~ treat, xformla=NULL, data=mpdta,
                panel=TRUE, first.treat.name="first.treat",
                idname="countyreal", tname="year",
                bstrap=FALSE, se=TRUE, cband=FALSE)
summary(out2)

# }