Model-free Treatment Effect Estimators

The didnpbsctest command contains tools for computing both heterogenous and average treatment effects for the treated in a model-free differences-in-differences framework.

Usage

didnpbsctest(...)

# S3 method for class 'formula'
didnpbsctest(
  formula,
  data = stop("argument 'data' is missing"),
  subset,
  boot.num = 399,
  print.level = 1,
  digits = 4,
  cores = 1,
  seed = 17345168,
  ...
)

# Default S3 method
didnpbsctest(
  outcome,
  regressors,
  time,
  treated,
  treatment_period,
  weights = NULL,
  boot.num = 399,
  print.level = 1,
  digits = 4,
  cores = 1,
  seed = 17345168,
  ...
)

Arguments

formula: an object of class formula (or one that can be coerced to that class): a symbolic description of the model. The details of model specification are given under `Details'
data: name of the data frame; must be specified if the 'formula' method is used
subset: NULL, optional subsample of 'data'
boot.num: an single value specifying the number of bootstrap replications. Default is 399.
print.level: the level of printing; larger number implies more output is printed. Default is 1. 0 suppresses all printing.
cores: Integer specifies the number of cores to be used for parallel computation.
seed: integer used for the random number generation for the replication purposes. Default is 17345168.
outcome: a vector, matrix, or data frame of length \(NT\). The outcome can be a continuous or dummy variable.
regressors: a data frame with \(NT\) rows that contains regressors. A data frame class is required to identify the type/class of each regressor.
time: a vector, matrix, or data frame of length \(NT\) that specifies in which period id is observed.
treatment_period: a vector, matrix, or data frame of length \(NT\) with zeros for the period before treatment and ones for the period of treatment and after.
weights: NULL,
treatment: a vector, matrix, or data frame of length \(NT\) with zeros for the control and ones for the treated observations.

Value

didnpbsctest returns a list containing:

`NT`	Total number of observations

`esample`	A vector of TRUE/FALSE values identifying observations used in estimation. Relevant for the 'formula' method but complete cases will also be checked in the matrix method

`sample1`	A vector of TRUE/FALSE values identifying treated observations.

`sample11`	A vector of TRUE/FALSE values identifying treated observations right after the treatement

`sample10`	A vector of TRUE/FALSE values identifying treated observations just before the treatment

`sample01`	A vector of TRUE/FALSE values identifying observations in control group right after the treatement

`sample00`	A vector of TRUE/FALSE values identifying observations in control group just before the treatement

`n11`	A number of treated observations right after the treatment

`n10`	A number of treated observations just before the treatment

`n01`	A number of observations in control group right after the treatment

`n00`	A number of observations in control group just before the treatment

`regressor.type`	A vector of length 3 with number of continuous, unordered categorical, and ordered categorical regressors.

`bw.time`	Time in seconds it took to calculate bandwidths. For bandwidth type "opt" is 0.

`boot.time`	Time in seconds it took to bootstrap the standard errors.

`boot.num`	Number of bootstrap replications.

`bw11`	Bandwidths calculated for the sample of treated right after the treatment.

`bw10`	Bandwidths calculated for the sample of treated just before the treatment.

`bw01`	Bandwidths calculated for the sample of of observations in control group right after the treatment.

`bw00`	Bandwidths calculated for the sample of observations in control group just before the treatement

Details

The formula shell contain multiple parts separated by '|'. An example is

weights can be omitted if not available

form1 <- y ~ x1 + x2 | id | time | treatment | treatment_period

References

... (...). This. Journal of , 1(1), 1-1 https://doi.org/10.

Author

Oleg Badunenko oleg.badunenko@brunel.ac.uk,

Daniel J. Henderson djhender@cba.ua.edu,

Stefan Sperlich stefan.sperlich@unige.ch

Examples

if (FALSE) { # \dontrun{
  data(DACAsub, package = "didnp")
  # will get a data frame 'DACAsub' with 330106 rows and 18 columns

  # get the subsample
  DACAsub$mysmpl <- mysmpl <-
    DACAsub$a1922==1 & !is.na(DACAsub$a1922) &
    DACAsub$htus==1 & !is.na(DACAsub$htus)

  # generate 'treatment_period'
  DACAsub$treatment_period <- ifelse(DACAsub[,"year"]>2011,1,0)

  # define formula with the weight
  form1 <- inschool ~ fem + race + var.bpl + state + age + yrimmig +
    ageimmig | inschool | year | elig | treatment_period | perwt

  # or without the weight
  form11 <- inschool ~ fem + race + var.bpl + state + age + yrimmig +
    ageimmig | inschool | year | elig | treatment_period

  ## Syntax using formula
  # suppress output
  tym1a <- didnpbsctest(
    form1,
    data = DACAsub,
    subset = mysmpl,
    bwmethod = "opt",
    boot.num = 399,
    TTb = FALSE,
    print.level = 0,
    cores = 4)

  # Print the summary
  summary(tym1a)

  ## Use CV bandwidths
  tym1aCV <- didnpbsctest(
    form1,
    data = DACAsub,
    subset = mysmpl,
    bwmethod = "CV",
    boot.num = 399,
    TTb = FALSE,
    print.level = 1,
    cores = 4)

  # Print the summary
  summary(tym1aCV)

  ## Calculate also TTb (will take longer)
  tym1bCV <- didnpbsctest(
    form1,
    data = DACAsub,
    subset = mysmpl,
    bwmethod = "CV",
    boot.num = 399,
    TTb = TRUE,
    print.level = 1,
    cores = 4)

  # Print the summary
  summary(tym1bCV)

  ## Syntax using matrices

  tym1aM <- didnpbsctest(
    outcome = DACAsub[mysmpl,"inschool"],
    regressors = DACAsub[mysmpl,c("fem", "race", "var.bpl", "state", "age", "yrimmig", "ageimmig")],
    id = DACAsub[mysmpl,"inschool"],
    time = DACAsub[mysmpl,"year"],
    treatment = DACAsub[mysmpl,"elig"],
    treatment_period = ifelse(DACAsub[mysmpl,"year"]>2011,1,0),
    weights = DACAsub[mysmpl,"perwt"],
    bwmethod = "opt",
    boot.num = 399,
    TTb = FALSE,
    print.level = 1,
    cores = 4)

  # Print the summary
  summary(tym1aM)

} # }