placebo_test {PanelMatch}

placebo_test

Description

Calculates results for a placebo test

Usage

placebo_test(
  pm.obj,
  data,
  lag.in = NULL,
  number.iterations = 1000,
  confidence.level = 0.95,
  plot = FALSE,
  se.method = "bootstrap",
  parallel = FALSE,
  num.cores = 1,
  ...
)

Arguments

pm.obj

an object of class PanelMatch

data

data.frame with the original data

lag.in

integer indicating earliest the time period(s) in the future for which the placebo test change in outcome will be calculated. Calculations will be made over the period t - max(lag) to t-2, where t is the time of treatment. The results are similar to those returned by PanelEstimate(), except t-1 is used as the period of comparison, rather than the lead window. If not specified, the placebo test is conducted for periods from t - max(lag) to t-2.

number.iterations

integer specifying the number of bootstrap iterations

confidence.level

confidence level for the calculated standard error intervals

plot

logical indicating whether or not a plot should be generated, or just return the raw data from the calculations

se.method

character string describing the type of standard error to be used. Valid inputs include "bootstrap", "conditional" and "unconditional". When the QOI is ATE, only bootstrap can be used.

parallel

Logical. If TRUE and se.method = ``bootstrap'', bootstrap procedure will be parallelized. Default is FALSE. If se.method is not set to bootstrap, this option does nothing.

num.cores

Integer. Specifies the number of cores to use for parallelization. If se.method = ``bootstrap'' and parallel = TRUE, then this option will take effect. Otherwise, it will do nothing.

...

extra arguments to be passed to plot()

Details

Calculate the results of a placebo test, looking at the change in outcome at time = t-1, compared to other pre-treatment periods in the lag window.

Value

list with 2 or 3 elements: "estimates", which contains the point estimates for the test, "standard.errors" which has the standard errors for each period and optionally "bootstrapped.estimates", containing the bootstrapped point estimates for the test for each specified lag window period.

Examples

dem.sub <- dem[dem[, "wbcode2"] <= 100, ]
# create subset of data for simplicity
PM.results <- PanelMatch(lag = 4, time.id = "year", unit.id = "wbcode2",
                         treatment = "dem", refinement.method = "mahalanobis",
                         data = dem.sub, match.missing = TRUE,
                         covs.formula = ~ tradewb,
                         size.match = 5, qoi = "att",
                         outcome.var = "y", lead = 0:4, forbid.treatment.reversal = FALSE,
                         placebo.test = TRUE)
placebo_test(PM.results, data = dem.sub, se.method = "unconditional", plot = FALSE)