conditional_did_pretest: Pre-Test of Conditional Parallel Trends Assumption

In did: Treatment Effects with Multiple Periods and Groups

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. 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.
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	Not used by the pre-test. Critical values and p-values for the Cramer von Mises and Kolmogorov-Smirnov test statistics are always computed with the multiplier bootstrap (using biters iterations), regardless of this argument, and no 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 multiplier bootstrap iterations used to simulate the limiting distribution of the test statistics. The default is 1000.
clustervars	A vector of variables names to cluster on (the multiplier bootstrap then draws cluster-level multipliers). 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.
est_method	the method to compute group-time average treatment effects. The default for conditional_did_pretest is "ipw" for inverse probability weighting. Other built-in methods include "dr" which uses the doubly robust approach in the DRDID package and "reg" for first step regression estimators.
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. This parallelizes Step 1 (computing the test statistic); Step 2's multiplier bootstrap is vectorized and runs in a single process.

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)

did documentation built on June 13, 2026, 5:07 p.m.