Computes UNDID results — undid_stage

Takes in all of the filled diff df CSV files and uses them to compute group level ATTs as well as the aggregate ATT and its standard errors and p-values. Also takes in the trends data CSV files and uses them to produce parallel trends and event study plots.

Usage

undid_stage_three(
  dir_path,
  agg = "g",
  weights = "both",
  covariates = FALSE,
  notyet = FALSE,
  nperm = 999,
  verbose = 100,
  check_anon_size = FALSE,
  hc = "hc1",
  only = NULL,
  omit = NULL,
  max_attempts = 100
)

Arguments

dir_path: A character specifying the filepath to the folder containing all of the filled diff df CSV files.
agg: A character which specifies the aggregation methodology for computing the aggregate ATT in the case of staggered adoption. Options are: "silo", "g", "gt", "sgt", "time", "none". Defaults to "g". "silo" computes a subaggregate ATT for each silo, "g" computes a subaggregate ATT for each unique treatment time, "gt" computes a subaggregate ATT for each unique treatment time & post-treatment period pair, "sgt" computes a subaggregate ATT for each treatment time & post-treatment pair, separated by silo, "time" computes subaggregate ATTs for grouped by time since treatment, and "none" does not compute any subaggregate ATTs, but rather computes an aggregate ATT directly from the differences.
weights: A string, determines which of the weighting methodologies should be used. Options are: "none", "diff", "att", or "both". Defaults to the weighting choice specified in the filled diff CSV files.
covariates: A logical value (either TRUE or FALSE) which specifies whether to use the diff_estimate column or the diff_estimate_covariates column from the filled diff df CSV files when computing ATTs.
notyet: A logical value which declares if the not-yet-treated differences from treated silos should be used as controls when computing relevant sub-aggregate ATTs. Defaults to FALSE.
nperm: Number of random permutations of treatment assignment to use when calculating the randomization inference p-value. Defaults to 999.
verbose: A numeric value (or NULL) which toggles messages showing the progress of the randomization inference once every verbose iterations. Defaults to 100.
check_anon_size: A logical value, which if TRUE displays which silos enabled the anonymize_weights argument in stage two, and their respective anonymize_size values. Defaults to FALSE.
hc: Specify which heteroskedasticity-consistent covariance matrix estimator (HCCME) should be used. Options are 0, 1, 2, 3, and 4 (or "hc0", "hc1", "hc2", "hc3", "hc4"). Defaults to "hc1".
only: A character vector of silos to include. Defaults to NULL.
omit: A character vector of silos to omit. Defaults to NULL.
max_attempts: A numeric value. Sets the maximum number of attempts to find a new unique random permutations during the randomization inference procedure. Defaults to 100.

Value

An UnDiDObj with S3 methods of summary(), plot(), print(), and coef().

Examples


# Execute `undid_stage_three()`
dir <- system.file("extdata/staggered", package = "undidR")
# \donttest{
   # Recommended: nperm >= 399 for reasonable precision
   # (~15 seconds on typical hardware)
   result <- undid_stage_three(dir, agg = "g", nperm = 399, verbose = NULL)

   # View the summary of results
   summary(result)
#> 
#>   Weighting: both
#>   Aggregation: g
#>   Not-yet-treated: FALSE
#>   Covariates: none
#>   HCCME: hc1
#>   Period Length: 1 year
#>   First Period: 1989
#>   Last Period: 2000
#>   Permutations: 399
#> 
#> Aggregate Results:
#>         ATT Std. Error   p-value RI p-value Jackknife SE Jackknife p-value
#>  0.07611833 0.03610753 0.1027101 0.03007519   0.04125346        0.09208696
#> 
#> Subaggregate Results:
#> Treatment Time              ATT         SE    p-value   RI p-val      JK SE   JK p-val     Weight
#> -------------------------------------------------------------------------------------------------------------- 
#> 1991                     0.0339     0.0253     0.1845     0.4511         NA         NA     0.2428
#> 1993                     0.0316     0.0243     0.1991     0.6015         NA         NA     0.2305
#> 1996                     0.0685     0.0360     0.0658     0.5288         NA         NA     0.0910
#> 1997                     0.1487     0.0301     0.0000     0.0426     0.0470     0.0090     0.3863
#> 1998                    -0.0623     0.0486     0.2155     0.4812         NA         NA     0.0494

   # View the parallel trends plot
   plot(result)


   # View the event study plot
   plot(result, event = TRUE)

# }