QTE function

qte: A package for computating quantile treatment effects

Main class of objects. A QTE object is returned by all of the methods that compute the QTE or QTET. package

QTE(
  qte,
  ate = NULL,
  qte.se = NULL,
  qte.lower = NULL,
  qte.upper = NULL,
  ate.se = NULL,
  ate.lower = NULL,
  ate.upper = NULL,
  c = NULL,
  pscore.reg = NULL,
  probs,
  type = "On the Treated",
  F.treated.t = NULL,
  F.untreated.t = NULL,
  F.treated.t.cf = NULL,
  F.treated.tmin1 = NULL,
  F.treated.tmin2 = NULL,
  F.treated.change.tmin1 = NULL,
  F.untreated.change.t = NULL,
  F.untreated.change.tmin1 = NULL,
  F.untreated.tmin1 = NULL,
  F.untreated.tmin2 = NULL,
  condQ.treated.t = NULL,
  condQ.treated.t.cf = NULL,
  eachIterList = NULL,
  inffunct = NULL,
  inffuncu = NULL
)

Arguments

• qte: The Quantile Treatment Effect at each value of probs
• ate: The Average Treatment Effect (or Average Treatment Effect on the Treated)
• qte.se: A vector of standard errors for each qte
• qte.lower: A vector of lower confidence intervals for each qte (it is based on the bootstrap confidence interval -- not the se -- so it may not be symmyetric about the qte
• qte.upper: A vector of upper confidence intervals for each qte (it is based on the bootstrap confidence interval -- not the se -- so it may not be symmetric about the qte
• ate.se: The standard error for the ATE
• ate.lower: Lower confidence interval for the ATE (it is based on the bootstrap confidence intervall -- not the se -- so it may not be symmetric about the ATE
• ate.upper: Upper confidence interval for the ATE (it is based on the bootstrap confidence interval -- not the se -- so it may not be symmetric about the ATE
• c: The critical value from a KS-type statistic used for creating uniform confidence bands
• pscore.reg: The results of propensity score regression, if specified
• probs: The values for which the qte is computed
• type: Takes the values "On the Treated" or "Population" to indicate whether the estimated QTE is for the treated group or for the entire population
• F.treated.t: Distribution of treated outcomes for the treated group at period t
• F.untreated.t: Distribution of untreated potential outcomes for the untreated group at period t
• F.treated.t.cf: Counterfactual distribution of untreated potential outcomes for the treated group at period t
• F.treated.tmin1: Distribution of treated outcomes for the treated group at period tmin1
• F.treated.tmin2: Distribution of treated outcomes for the treated group at period tmin2
• F.treated.change.tmin1: Distribution of the change in outcomes for the treated group between periods tmin1 and tmin2
• F.untreated.change.t: Distribution of the change in outcomes for the untreated group between periods t and tmin1
• F.untreated.change.tmin1: Distribution of the change in outcomes for the untreated group between periods tmin1 and tmin2
• F.untreated.tmin1: Distribution of outcomes for the untreated group in period tmin1
• F.untreated.tmin2: Distribution of outcomes for the untreated group in period tmin2
• condQ.treated.t: Conditional quantiles for the treated group in period t
• condQ.treated.t.cf: Counterfactual conditional quantiles for the treated group in period t
• eachIterList: An optional list of the outcome of each bootstrap iteration
• inffunct: The influence function for the treated group; used for inference when there are multiple periods and in the case with panel data. It is needed for computing covariance terms in the variance-covariance matrix.
• inffuncu: The influence function for the untreated group