Package 'qte'

Title:	Quantile Treatment Effects
Description:	Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <doi:10.3982/QE935>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).
Authors:	Brantly Callaway [aut, cre]
Maintainer:	Brantly Callaway <[email protected]>
License:	GPL-2
Version:	1.4.0
Built:	2025-01-25 04:20:53 UTC
Source:	https://github.com/bcallaway11/qte

bounds

Description

bounds estimates bounds for the Quantile Treatment Effect on the Treated (QTET) using the method of Fan and Yu (2012).

Usage

bounds(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  idname,
  probs = seq(0.05, 0.95, 0.05)
)
bounds(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  idname,
  probs = seq(0.05, 0.95, 0.05)
)

Arguments

`formla`	The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated
`xformla`	A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
`t`	The 3rd time period in the sample. Treated individuals should be treated in this time period and untreated individuals should not be treated. The code attempts to enforce this condition, but it is good try to handle this outside the panel.qtet method.
`tmin1`	The 2nd time period in the sample. This should be a pre-treatment period for all individuals in the sample.
`tname`	The name of the column containing the time periods
`data`	A data.frame containing all the variables used
`idname`	The individual (cross-sectional unit) id name
`probs`	A vector of values between 0 and 1 to compute the QTET at

Value

A BoundsObj object

References

Fan, Yanqin and Zhengfei Yu. “Partial Identification of Distributional and Quantile Treatment Effects in Difference-in-Differences Models.” Economics Letters 115.3, pp.511-515, 2012.

Examples

## load the data
data(lalonde)

## Run the bounds method with no covariates
b1 <- bounds(re ~ treat, t=1978, tmin1=1975, data=lalonde.psid.panel,
  idname="id", tname="year")
summary(b1)

## load the data
data(lalonde)

## Run the bounds method with no covariates
b1 <- bounds(re ~ treat, t=1978, tmin1=1975, data=lalonde.psid.panel,
  idname="id", tname="year")
summary(b1)

The ci.qtet method implements estimates the Quantile Treatment Effect (QTE) under a Conditional Independence Assumption (sometimes this is called Selection on Observables) developed in Firpo (2007). This method using propensity score re-weighting and minimizes a check function to compute the QTET. Standard errors (if requested) are computed using the bootstrap.

Usage

ci.qte(
  formla,
  xformla = NULL,
  x = NULL,
  data,
  w = NULL,
  probs = seq(0.05, 0.95, 0.05),
  se = TRUE,
  iters = 100,
  alp = 0.05,
  method = "logit",
  retEachIter = FALSE,
  printIter = FALSE,
  pl = FALSE,
  cores = 2
)
ci.qte(
  formla,
  xformla = NULL,
  x = NULL,
  data,
  w = NULL,
  probs = seq(0.05, 0.95, 0.05),
  se = TRUE,
  iters = 100,
  alp = 0.05,
  method = "logit",
  retEachIter = FALSE,
  printIter = FALSE,
  pl = FALSE,
  cores = 2
)

Arguments

`formla`	The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated
`xformla`	A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
`x`	Vector of covariates. Default is no covariates
`data`	A data.frame containing all the variables used
`w`	an additional vector of sampling weights
`probs`	A vector of values between 0 and 1 to compute the QTET at
`se`	Boolean whether or not to compute standard errors
`iters`	The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
`alp`	The significance level used for constructing bootstrap confidence intervals
`method`	Method to compute propensity score. Default is logit; other option is probit.
`retEachIter`	Boolean whether or not to return list of results from each iteration of the bootstrap procedure (default is FALSE). This is potentially useful for debugging but can cause errors due to running out of memory.
`printIter`	For debugging only; should leave at default FALSE unless you want to see a lot of output
`pl`	boolean for whether or not to compute bootstrap error in parallel. Note that computing standard errors in parallel is a new feature and may not work at all on Windows.
`cores`	the number of cores to use if bootstrap standard errors are computed in parallel

Value

QTE object

References

Firpo, Sergio. “Efficient Semiparametric Estimation of Quantile Treatment Effects.” Econometrica 75.1, pp. 259-276, 2015.

Examples

## Load the data
data(lalonde)

##Estimate the QTET of participating in the job training program;
##This is the no covariate case.  Note: Because individuals that participate
## in the job training program are likely to be much different than
## individuals that do not (e.g. less experience and less education), this
## method is likely to perform poorly at estimating the true QTET
q1 <- ci.qte(re78 ~ treat, x=NULL, data=lalonde.psid, se=FALSE,
 probs=seq(0.05,0.95,0.05))
summary(q1)

##This estimation controls for all the available background characteristics.
q2 <- ci.qte(re78 ~ treat,
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid, se=FALSE, probs=seq(0.05, 0.95, 0.05))
summary(q2)

## Load the data
data(lalonde)

##Estimate the QTET of participating in the job training program;
##This is the no covariate case.  Note: Because individuals that participate
## in the job training program are likely to be much different than
## individuals that do not (e.g. less experience and less education), this
## method is likely to perform poorly at estimating the true QTET
q1 <- ci.qte(re78 ~ treat, x=NULL, data=lalonde.psid, se=FALSE,
 probs=seq(0.05,0.95,0.05))
summary(q1)

##This estimation controls for all the available background characteristics.
q2 <- ci.qte(re78 ~ treat,
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid, se=FALSE, probs=seq(0.05, 0.95, 0.05))
summary(q2)

ci.qtet

Description

The ci.qtet method implements estimates the Quantile Treatment Effect on the Treated (QTET) under a Conditional Independence Assumption (sometimes this is called Selection on Observables) developed in Firpo (2007). This method using propensity score re-weighting and minimizes a check function to compute the QTET. Standard errors (if requested) are computed using the bootstrap.

Usage

ci.qtet(
  formla,
  xformla = NULL,
  w = NULL,
  data,
  probs = seq(0.05, 0.95, 0.05),
  se = TRUE,
  iters = 100,
  alp = 0.05,
  method = "logit",
  retEachIter = FALSE,
  indsample = TRUE,
  printIter = FALSE,
  pl = FALSE,
  cores = 2
)
ci.qtet(
  formla,
  xformla = NULL,
  w = NULL,
  data,
  probs = seq(0.05, 0.95, 0.05),
  se = TRUE,
  iters = 100,
  alp = 0.05,
  method = "logit",
  retEachIter = FALSE,
  indsample = TRUE,
  printIter = FALSE,
  pl = FALSE,
  cores = 2
)

Arguments

`formla`	The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated
`xformla`	A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
`w`	an additional vector of sampling weights
`data`	A data.frame containing all the variables used
`probs`	A vector of values between 0 and 1 to compute the QTET at
`se`	Boolean whether or not to compute standard errors
`iters`	The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
`alp`	The significance level used for constructing bootstrap confidence intervals
`method`	Method to compute propensity score. Default is logit; other option is probit.
`retEachIter`	Boolean whether or not to return list of results from each iteration of the bootstrap procedure (default is FALSE). This is potentially useful for debugging but can cause errors due to running out of memory.
`indsample`	Binary variable for whether to treat the samples as independent or dependent. This affects bootstrap standard errors. In the job training example, the samples are independent because they are two samples collected independently and then merged. If the data is from the same source, usually should set this option to be FALSE.
`printIter`	For debugging only; should leave at default FALSE unless you want to see a lot of output
`pl`	Whether or not to compute standard errors in parallel
`cores`	Number of cores to use if computing in parallel

Value

QTE object

References

Firpo, Sergio. “Efficient Semiparametric Estimation of Quantile Treatment Effects.” Econometrica 75.1, pp. 259-276, 2015.

Examples

## Load the data
data(lalonde)

##Estimate the QTET of participating in the job training program;
##This is the no covariate case.  Note: Because individuals that participate
## in the job training program are likely to be much different than
## individuals that do not (e.g. less experience and less education), this
## method is likely to perform poorly at estimating the true QTET
q1 <- ci.qtet(re78 ~ treat, x=NULL, data=lalonde.psid, se=FALSE,
 probs=seq(0.05,0.95,0.05))
summary(q1)

##This estimation controls for all the available background characteristics.
q2 <- ci.qtet(re78 ~ treat, 
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid, se=FALSE, probs=seq(0.05, 0.95, 0.05))
summary(q2)

## Load the data
data(lalonde)

##Estimate the QTET of participating in the job training program;
##This is the no covariate case.  Note: Because individuals that participate
## in the job training program are likely to be much different than
## individuals that do not (e.g. less experience and less education), this
## method is likely to perform poorly at estimating the true QTET
q1 <- ci.qtet(re78 ~ treat, x=NULL, data=lalonde.psid, se=FALSE,
 probs=seq(0.05,0.95,0.05))
summary(q1)

##This estimation controls for all the available background characteristics.
q2 <- ci.qtet(re78 ~ treat, 
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid, se=FALSE, probs=seq(0.05, 0.95, 0.05))
summary(q2)

Change in Changes

Description

CiC computes the Quantile Treatment Effect on the Treated (QTET) using the method of Athey and Imbens (2006). CiC is a Difference in Differences type method. It requires having two periods of data that can be either repeated cross sections or panel data.

The method can accommodate conditioning on covariates though it does so in a restrictive way: It specifies a linear model for outcomes conditional on group-time dummies and covariates. Then, after residualizing (see details in Athey and Imbens (2006)), it computes the Change in Changes model based on these quasi-residuals.

Usage

CiC(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  panel = FALSE,
  se = TRUE,
  idname = NULL,
  alp = 0.05,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  pl = FALSE,
  cores = 2,
  retEachIter = FALSE
)
CiC(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  panel = FALSE,
  se = TRUE,
  idname = NULL,
  alp = 0.05,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  pl = FALSE,
  cores = 2,
  retEachIter = FALSE
)

Arguments

`formla`	The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated
`xformla`	A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
`t`	The 3rd time period in the sample. Treated individuals should be treated in this time period and untreated individuals should not be treated. The code attempts to enforce this condition, but it is good try to handle this outside the panel.qtet method.
`tmin1`	The 2nd time period in the sample. This should be a pre-treatment period for all individuals in the sample.
`tname`	The name of the column containing the time periods
`data`	A data.frame containing all the variables used
`panel`	Binary variable indicating whether or not the dataset is panel. This is used for computing bootstrap standard errors correctly.
`se`	Boolean whether or not to compute standard errors
`idname`	The individual (cross-sectional unit) id name
`alp`	The significance level used for constructing bootstrap confidence intervals
`probs`	A vector of values between 0 and 1 to compute the QTET at
`iters`	The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
`pl`	Whether or not to compute standard errors in parallel
`cores`	Number of cores to use if computing in parallel
`retEachIter`	Boolean whether or not to return list of results from each iteration of the bootstrap procedure (default is FALSE). This is potentially useful for debugging but can cause errors due to running out of memory.

Value

QTE Object

References

Athey, Susan and Guido Imbens. “Identification and Inference in Nonlinear Difference-in-Differences Models.” Econometrica 74.2, pp. 431-497, 2006.

Examples

## load the data
data(lalonde)
## Run the Change in Changes model conditioning on age, education,
## black, hispanic, married, and nodegree
c1 <- CiC(re ~ treat, t=1978, tmin1=1975, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(c1)


## load the data
data(lalonde)
## Run the Change in Changes model conditioning on age, education,
## black, hispanic, married, and nodegree
c1 <- CiC(re ~ treat, t=1978, tmin1=1975, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(c1)

cic_attgt

Description

cic_attgt

Usage

cic_attgt(gt_data, xformla = ~1, ...)
cic_attgt(gt_data, xformla = ~1, ...)

Arguments

`gt_data`	data that is "local" to a particular group-time average treatment effect
`xformla`	one-sided formula for covariates used in the propensity score and outcome regression models
`...`	extra function arguments; not used here

Value

pte::attgt_noif object. cic_attgt computes attgt using the CIC approach. It also returns distributions of observed outcomes for the treated group (F1), the counterfactual distribution of untreated potential potential outcomes for the treated group (F0), and the distribution of the treatment effect under the assumption of rank invariance over time (Fte) all through the extra_gt_returns argument to pte::attgt_noif object.

cic2

Description

This is a multi-period implementation of the change-in-changes approach from Athey and Imbens (2006, Econometrica). This function is in a beta release and users should use caution when using this function in emprical work.

The function builds on the pte package and will return an overall treatment effect parameter as well as an event study. See, in particular, the argument ret_quantile below.

Usage

cic2(
  yname,
  gname,
  tname,
  idname,
  data,
  xformla = ~1,
  ret_quantile = NULL,
  gt_type = "att",
  anticipation = 0,
  cband = TRUE,
  alp = 0.05,
  boot_type = "empirical",
  biters = 100,
  cl = 1
)
cic2(
  yname,
  gname,
  tname,
  idname,
  data,
  xformla = ~1,
  ret_quantile = NULL,
  gt_type = "att",
  anticipation = 0,
  cband = TRUE,
  alp = 0.05,
  boot_type = "empirical",
  biters = 100,
  cl = 1
)

Arguments

`yname`	Name of outcome in `data`
`gname`	Name of group in `data`
`tname`	Name of time period in `data`
`idname`	Name of id in `data`
`data`	balanced panel data
`ret_quantile`	This parameter determines which quantile will be reported by the cic2 function. By default `ret_quantile=NULL`; in this case, the function will return an estimate of the overall ATT and an event study for the ATT. Other choices should be between 0 and 1. For example, if the user specifies `ret_quantile=0.9`, then the function will return overall and event study parameters for the QTT(0.9). These ...would be better to return the overall distribution and then to average and invert in later steps...
`alp`	significance level; default is 0.05
`boot_type`	should be one of "multiplier" (the default) or "empirical". The multiplier bootstrap is generally much faster, but `attgt_fun` needs to provide an expression for the influence function (which could be challenging to figure out). If no influence function is provided, then the `pte` package will use the empirical bootstrap no matter what the value of this parameter.
`biters`	number of bootstrap iterations; default is 100
`cl`	number of clusters to be used when bootstrapping; default is 1
`ret_dist`	If set to be true, the function returns the observed distribution of outcomes and counterfactual distribution of outcomes for each (g,t) through the `extra_gt_returns` element of `group_time_att` object.

compute.panel.qtet

Description

compute.panel.qtet uses third period of data, combined with Distributional Difference in Differences assumption (Fan and Yu, 2012) to point identify QTET.

Usage

compute.panel.qtet(qp)
compute.panel.qtet(qp)

Arguments

`qp`	QTEparams object containing the parameters passed to ciqte

Value

QTE object

computeDiffSE

Description

Takes two sets of initial estimates and bootstrap estimations (they need to have the same number of iterations) and determines whether or not the estimates are statistically different from each other. It can be used to compare any sets of estimates, but it is particularly used here to compare estimates from observational methods with observations from the experimental data (which also have standard errors because, even though the estimates are cleanly identified, they are still estimated).

Usage

computeDiffSE(est1, bootIters1, est2, bootIters2, alp = 0.05)
computeDiffSE(est1, bootIters1, est2, bootIters2, alp = 0.05)

Arguments

`est1`	A QTE object containing the first set of estimates
`bootIters1`	A List of QTE objects that have been bootstrapped
`est2`	A QTE object containing a second set of estimates
`bootIters2`	A List of QTE objects that have been bootstrapped using the second method
`alp`	The significance level used for constructing bootstrap confidence intervals

ddid2

Description

ddid2 computes the Quantile Treatment Effect on the Treated (QTET) using the method of Callaway, Li, and Oka (2015).

Usage

ddid2(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  panel = TRUE,
  dropalwaystreated = TRUE,
  idname = NULL,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  alp = 0.05,
  method = "logit",
  se = TRUE,
  retEachIter = FALSE,
  seedvec = NULL,
  pl = FALSE,
  cores = NULL
)
ddid2(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  panel = TRUE,
  dropalwaystreated = TRUE,
  idname = NULL,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  alp = 0.05,
  method = "logit",
  se = TRUE,
  retEachIter = FALSE,
  seedvec = NULL,
  pl = FALSE,
  cores = NULL
)

Arguments

`formla`	The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary)
`xformla`	A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
`t`	The 3rd time period in the sample (this is the name of the column)
`tmin1`	The 2nd time period in the sample (this is the name of the column)
`tname`	The name of the column containing the time periods
`data`	The name of the data.frame that contains the data
`panel`	Boolean indicating whether the data is panel or repeated cross sections
`dropalwaystreated`	How to handle always treated observations in panel data case (not currently used)
`idname`	The individual (cross-sectional unit) id name
`probs`	A vector of values between 0 and 1 to compute the QTET at
`iters`	The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
`alp`	The significance level used for constructing bootstrap confidence intervals
`method`	The method for estimating the propensity score when covariates are included
`se`	Boolean whether or not to compute standard errors
`retEachIter`	Boolean whether or not to return list of results from each iteration of the bootstrap procedure
`seedvec`	Optional value to set random seed; can possibly be used in conjunction with bootstrapping standard errors.
`pl`	boolean for whether or not to compute bootstrap error in parallel. Note that computing standard errors in parallel is a new feature and may not work at all on Windows.
`cores`	the number of cores to use if bootstrap standard errors are computed in parallel

Value

QTE object

References

Callaway, Brantly, Tong Li, and Tatsushi Oka. “Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with Only Two Time Periods.” Working Paper, 2015.

Examples

##load the data
data(lalonde)

## Run the ddid2 method on the observational data with no covariates
d1 <- ddid2(re ~ treat, t=1978, tmin1=1975, tname="year",
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(d1)

## Run the ddid2 method on the observational data with covariates
d2 <- ddid2(re ~ treat, t=1978, tmin1=1975, tname="year",
 data=lalonde.psid.panel, idname="id", se=FALSE,
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 probs=seq(0.05, 0.95, 0.05))
summary(d2)


##load the data
data(lalonde)

## Run the ddid2 method on the observational data with no covariates
d1 <- ddid2(re ~ treat, t=1978, tmin1=1975, tname="year",
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(d1)

## Run the ddid2 method on the observational data with covariates
d2 <- ddid2(re ~ treat, t=1978, tmin1=1975, tname="year",
 data=lalonde.psid.panel, idname="id", se=FALSE,
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 probs=seq(0.05, 0.95, 0.05))
summary(d2)

diffQ

Description

takes a single set of quantiles

(not qtes as in diffquantiles) and returns the difference between particular ones

Usage

diffQ(qvec, tauvec, hightau, lowtau)
diffQ(qvec, tauvec, hightau, lowtau)

Arguments

`qvec`	vector of quantiles
`tauvec`	vector of tau (should be same length as qvec)
`hightau`	upper quantile
`lowtau`	lower quantile

Value

scalar difference between quantiles

DR

Description

A distribution regression object

Usage

DR(yvals, drlist)
DR(yvals, drlist)

Arguments

`yvals`	A vector of values that y can take
`drlist`	A list where for each value of y, a distribution regression

ggqte

Description

Makes somewhat nicer plots of quantile treatment effects by using ggplot

Usage

ggqte(
  qteobj,
  main = "",
  ylab = "QTE",
  ylim = NULL,
  ybreaks = NULL,
  xbreaks = c(0.1, 0.3, 0.5, 0.7, 0.9),
  setype = "pointwise",
  alp = qteobj$alp
)
ggqte(
  qteobj,
  main = "",
  ylab = "QTE",
  ylim = NULL,
  ybreaks = NULL,
  xbreaks = c(0.1, 0.3, 0.5, 0.7, 0.9),
  setype = "pointwise",
  alp = qteobj$alp
)

Arguments

`qteobj`	a QTE object
`main`	optional title
`ylab`	optional y axis label
`ylim`	optional limits of y axis
`ybreaks`	optional breaks in y axis
`xbreaks`	optional breaks in x axis
`setype`	options are "pointwise", "uniform" or both; pointwise confidence intervals cover the QTE at each particular point with a fixed probability, uniform confidence bands cover the entire curve with a fixed probability. Uniform confidence bands will tend to be wider. The option "both" will plot both types of confidence intervals
`alp`	gives a way to override the significance level in the case where `setype="pointwise"`.

Value

a ggplot object

Lalonde (1986)'s NSW Dataset

Description

lalonde contains data from the National Supported Work Demonstration. This program randomly assigned applicants to the job training program (or out of the job training program). The dataset is discussed in Lalonde (1986). The experimental part of the dataset is combined with an observational dataset from the Panel Study of Income Dynamics (PSID). Lalonde (1986) and many subsequent papers (e.g. Heckman and Hotz (1989), Dehejia and Wahba (1999), Smith and Todd (2005), and Firpo (2007) have used this combination to study the effectiveness of various ‘observational’ methods (e.g. regression, Heckman selection, Difference in Differences, and propensity score matching) of estimating the Average Treatment Effect (ATE) of participating in the job training program. The idea is that the results from the observational method can be compared to results that can be easily obtained from the experimental portion of the dataset.

To be clear, the observational data combines the observations that are treated from the experimental portion of the data with untreated observations from the PSID.

Usage

data(lalonde)
data(lalonde)

Format

Four data.frames: (i) lalonde.exp contains a cross sectional version of the experimental data, (ii) lalonde.psid contains a cross sectional version of the observational data, (iii) lalonde.exp.panel contains a panel version of the experimental data, and (iv) lalonde.psid.panel contains a panel version of the observational data. Note: the cross sectional and panel versions of each dataset are identical up to their shape; in demonstrating each of the methods, it is sometimes convenient to have one form of the data or the other.

References

LaLonde, Robert. “Evaluating the Econometric Evaluations of Training Programs with Experimental Data.” The American Economics Review, pp. 604-620, 1986. @source The dataset comes from Lalonde (1986) and has been studied in much subsequent work. The qte package uses a version from the causalsens package (https://CRAN.R-project.org/package=causalsens)

Lalonde's Experimental Dataset

Description

The cross sectional verion of the experimental part of the lalonde dataset. It is loaded with all the datasets with the command data(lalonde)

Lalonde's Panel Experimental Dataset

Description

The panel verion of the experimental part of the lalonde dataset. It is loaded with all the datasets with the command data(lalonde)

Lalonde's Observational Dataset

Description

The cross sectional verion of the observational part of the lalonde dataset. It is loaded with all the datasets with the command data(lalonde)

Lalonde's Experimental Dataset

Description

The panel verion of the observational part of the lalonde dataset. It is loaded with all the datasets with the command data(lalonde)

Mean Difference in Differences

Description

MDiD is a Difference in Differences type method for computing the QTET.

Usage

MDiD(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  panel = FALSE,
  se = TRUE,
  idname = NULL,
  alp = 0.05,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  retEachIter = FALSE
)
MDiD(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  panel = FALSE,
  se = TRUE,
  idname = NULL,
  alp = 0.05,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  retEachIter = FALSE
)

Arguments

`formla`	The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated
`xformla`	A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
`t`	The 3rd time period in the sample. Treated individuals should be treated in this time period and untreated individuals should not be treated. The code attempts to enforce this condition, but it is good try to handle this outside the panel.qtet method.
`tmin1`	The 2nd time period in the sample. This should be a pre-treatment period for all individuals in the sample.
`tname`	The name of the column containing the time periods
`data`	A data.frame containing all the variables used
`panel`	Binary variable indicating whether or not the dataset is panel. This is used for computing bootstrap standard errors correctly.
`se`	Boolean whether or not to compute standard errors
`idname`	The individual (cross-sectional unit) id name
`alp`	The significance level used for constructing bootstrap confidence intervals
`probs`	A vector of values between 0 and 1 to compute the QTET at
`iters`	The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
`retEachIter`	Boolean whether or not to return list of results from each iteration of the bootstrap procedure (default is FALSE). This is potentially useful for debugging but can cause errors due to running out of memory.

Value

A QTE object

References

Athey, Susan and Guido Imbens. “Identification and Inference in Nonlinear Difference-in-Differences Models.” Econometrica 74.2, pp. 431-497, 2006.

Thuysbaert, Bram. “Distributional Comparisons in Difference in Differences Models.” Working Paper, 2007.

Examples

## load the data
data(lalonde)

## Run the Mean Difference in Differences method conditioning on
## age, education, black, hispanic, married, and nodegree
md1 <- MDiD(re ~ treat, t=1978, tmin1=1975, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(md1)

## load the data
data(lalonde)

## Run the Mean Difference in Differences method conditioning on
## age, education, black, hispanic, married, and nodegree
md1 <- MDiD(re ~ treat, t=1978, tmin1=1975, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(md1)

panel.checks

Description

Does some checking that data setup is valid for using methods in qte package

Usage

panel.checks(qp)
panel.checks(qp)

Arguments

`qp`	QTEparams object containing the parameters passed to ciqte

panel.qtet

Description

panel.qtet computes the Quantile Treatment Effect on the Treated (QTET) using the method of Callaway and Li (2015). This method should be used when the researcher wants to invoke a Difference in Differences assumption to identify the QTET. Relative to the other Difference in Differences methods available in the qte package, this method's assumptions are more intuitively similar to the identifying assumptions used in identifying the Average Treatment Effect on the Treated (ATT).

Additionally, this method can accommodate covariates in a more flexible way than the other Difference in Differences methods available. In order to accommodate covariates, the user should specify a vector x of covariate names. The user also may specify a method for estimating the propensity score. The default is logit.

panel.qtet can only be used in some situations, however. The method requires three periods of panel data where individuals are not treated until the last period. The data should be formatted as a panel; the names of columns containing time periods and ids for each cross sectional unit need to be passed to the method.

Usage

panel.qtet(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tmin2,
  tname,
  data,
  idname,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  alp = 0.05,
  method = c("qr", "pscore"),
  se = TRUE,
  retEachIter = FALSE,
  pl = FALSE,
  cores = NULL
)
panel.qtet(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tmin2,
  tname,
  data,
  idname,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  alp = 0.05,
  method = c("qr", "pscore"),
  se = TRUE,
  retEachIter = FALSE,
  pl = FALSE,
  cores = NULL
)

Arguments

`formla`	The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated
`xformla`	A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
`t`	The 3rd time period in the sample. Treated individuals should be treated in this time period and untreated individuals should not be treated. The code attempts to enforce this condition, but it is good try to handle this outside the panel.qtet method.
`tmin1`	The 2nd time period in the sample. This should be a pre-treatment period for all individuals in the sample.
`tmin2`	The 1st time period in the sample. This should be a pre-treatment period for all individuals in the sample.
`tname`	The name of the column containing the time periods
`data`	A data.frame containing all the variables used
`idname`	The individual (cross-sectional unit) id name
`probs`	A vector of values between 0 and 1 to compute the QTET at
`iters`	The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
`alp`	The significance level used for constructing bootstrap confidence intervals
`method`	The method for including covariates, should either be "QR" for quantile regression or "pscore" for propensity score
`se`	Boolean whether or not to compute standard errors
`retEachIter`	Boolean whether or not to return list of results from each iteration of the bootstrap procedure (default is FALSE). This is potentially useful for debugging but can cause errors due to running out of memory.
`pl`	Whether or not to compute standard errors in parallel
`cores`	Number of cores to use if computing in parallel

Value

QTE object

References

Callaway, Brantly and Tong Li. “Quantile Treatment Effects in Difference in Differences Models with Panel Data.” Working Paper, 2019.

Examples

##load the data
data(lalonde)

## Run the panel.qtet method on the experimental data with no covariates
pq1 <- panel.qtet(re ~ treat, t=1978, tmin1=1975, tmin2=1974, tname="year",
 data=lalonde.exp.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(pq1)

## Run the panel.qtet method on the observational data with no covariates
pq2 <- panel.qtet(re ~ treat, t=1978, tmin1=1975, tmin2=1974, tname="year",
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(pq2)

## Run the panel.qtet method on the observational data conditioning on
## age, education, black, hispanic, married, and nodegree.
## The propensity score will be estimated using the default logit method.
pq3 <- panel.qtet(re ~ treat, t=1978, tmin1=1975, tmin2=1974, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE, method="pscore",
 probs=seq(0.05, 0.95, 0.05))
summary(pq3)

pq4 <- panel.qtet(re ~ treat, t=1978, tmin1=1975, tmin2=1974, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE, method="qr",
 probs=seq(0.05, 0.95, 0.05))
summary(pq4)

##load the data
data(lalonde)

## Run the panel.qtet method on the experimental data with no covariates
pq1 <- panel.qtet(re ~ treat, t=1978, tmin1=1975, tmin2=1974, tname="year",
 data=lalonde.exp.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(pq1)

## Run the panel.qtet method on the observational data with no covariates
pq2 <- panel.qtet(re ~ treat, t=1978, tmin1=1975, tmin2=1974, tname="year",
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(pq2)

## Run the panel.qtet method on the observational data conditioning on
## age, education, black, hispanic, married, and nodegree.
## The propensity score will be estimated using the default logit method.
pq3 <- panel.qtet(re ~ treat, t=1978, tmin1=1975, tmin2=1974, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE, method="pscore",
 probs=seq(0.05, 0.95, 0.05))
summary(pq3)

pq4 <- panel.qtet(re ~ treat, t=1978, tmin1=1975, tmin2=1974, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE, method="qr",
 probs=seq(0.05, 0.95, 0.05))
summary(pq4)

panelize.data

Description

get data in correct format for using panel methods in qte package

Usage

panelize.data(data, idname, tname, t, tmin1, tmin2 = NULL)
panelize.data(data, idname, tname, t, tmin1, tmin2 = NULL)

Arguments

`data`	A data.frame containing all the variables used
`idname`	The individual (cross-sectional unit) id name
`tname`	The name of the column containing the time periods
`t`	The 3rd time period in the sample. Treated individuals should be treated in this time period and untreated individuals should not be treated. The code attempts to enforce this condition, but it is good try to handle this outside the panel.qtet method.
`tmin1`	The 2nd time period in the sample. This should be a pre-treatment period for all individuals in the sample.
`tmin2`	The 1st time period in the sample. This should be a pre-treatment period for all individuals in the sample.

Value

data.frame

Plot Bounds

Description

Plots a BoundObj Object

Usage

## S3 method for class 'BoundsObj'
plot(
  x,
  plotate = FALSE,
  plot0 = FALSE,
  qtecol = "black",
  atecol = "black",
  col0 = "black",
  ylim = NULL,
  uselegend = FALSE,
  legloc = "topright",
  ...
)
## S3 method for class 'BoundsObj'
plot(
  x,
  plotate = FALSE,
  plot0 = FALSE,
  qtecol = "black",
  atecol = "black",
  col0 = "black",
  ylim = NULL,
  uselegend = FALSE,
  legloc = "topright",
  ...
)

Arguments

`x`	A BoundsObj Object
`plotate`	Boolean whether or not to plot the ATE
`plot0`	Boolean whether to plot a line at 0
`qtecol`	Color for qte plot. Default "black"
`atecol`	Color for ate plot. Default "black"
`col0`	Color for 0 plot. Default "black"
`ylim`	The ylim for the plot; if not passed, it will be automatically set based on the values that the QTE takes
`uselegend`	Boolean whether or not to print a legend
`legloc`	String location for the legend. Default "topright"
`...`	Other parameters to be passed to plot (e.g lwd)

plot.QTE

Description

Plots a QTE Object

Usage

## S3 method for class 'QTE'
plot(
  x,
  plotate = FALSE,
  plot0 = FALSE,
  qtecol = "black",
  atecol = "black",
  col0 = "black",
  xlab = "tau",
  ylab = "QTE",
  legend = NULL,
  ontreated = FALSE,
  ylim = NULL,
  uselegend = FALSE,
  legendcol = NULL,
  legloc = "topright",
  ...
)
## S3 method for class 'QTE'
plot(
  x,
  plotate = FALSE,
  plot0 = FALSE,
  qtecol = "black",
  atecol = "black",
  col0 = "black",
  xlab = "tau",
  ylab = "QTE",
  legend = NULL,
  ontreated = FALSE,
  ylim = NULL,
  uselegend = FALSE,
  legendcol = NULL,
  legloc = "topright",
  ...
)

Arguments

`x`	a QTE Object
`plotate`	Boolean whether or not to plot the ATE
`plot0`	Boolean whether to plot a line at 0
`qtecol`	Color for qte plot. Default "black"
`atecol`	Color for ate plot. Default "black"
`col0`	Color for 0 plot. Default "black"
`xlab`	Custom label for x-axis. Default "tau"
`ylab`	Custom label for y-axis. Default "QTE"
`legend`	Vector of strings to add to legend
`ontreated`	Boolean whether parameters are "on the treated group"
`ylim`	The ylim for the plot; if not passed, it will be automatically set based on the values that the QTE takes
`uselegend`	Boolean whether or not to print a legend
`legendcol`	Legend Colors for plotting
`legloc`	String location for the legend. Default "topright"
`...`	Other parameters to be passed to plot (e.g lwd)

Print a summary.BoundsObj

Description

Prints a Summary QTE Object

Usage

## S3 method for class 'summary.BoundsObj'
print(x, ...)
## S3 method for class 'summary.BoundsObj'
print(x, ...)

Arguments

`x`	A summary.BoundsObj
`...`	Other objects to pass (not used)

Print

Description

Prints a Summary QTE Object

Usage

## S3 method for class 'summary.QTE'
print(x, ...)
## S3 method for class 'summary.QTE'
print(x, ...)

Arguments

`x`	A summary.QTE object
`...`	Other params (required as generic function, but not used)

Quantile Difference in Differences

Description

QDiD is a Difference in Differences type method for computing the QTET.

Usage

QDiD(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  panel = FALSE,
  se = TRUE,
  idname = NULL,
  alp = 0.05,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  retEachIter = FALSE,
  pl = FALSE,
  cores = NULL
)
QDiD(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  panel = FALSE,
  se = TRUE,
  idname = NULL,
  alp = 0.05,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  retEachIter = FALSE,
  pl = FALSE,
  cores = NULL
)

Arguments

`formla`	The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated
`xformla`	A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
`t`	The 3rd time period in the sample. Treated individuals should be treated in this time period and untreated individuals should not be treated. The code attempts to enforce this condition, but it is good try to handle this outside the panel.qtet method.
`tmin1`	The 2nd time period in the sample. This should be a pre-treatment period for all individuals in the sample.
`tname`	The name of the column containing the time periods
`data`	A data.frame containing all the variables used
`panel`	Binary variable indicating whether or not the dataset is panel. This is used for computing bootstrap standard errors correctly.
`se`	Boolean whether or not to compute standard errors
`idname`	The individual (cross-sectional unit) id name
`alp`	The significance level used for constructing bootstrap confidence intervals
`probs`	A vector of values between 0 and 1 to compute the QTET at
`iters`	The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
`retEachIter`	Boolean whether or not to return list of results from each iteration of the bootstrap procedure (default is FALSE). This is potentially useful for debugging but can cause errors due to running out of memory.
`pl`	Whether or not to compute standard errors in parallel
`cores`	Number of cores to use if computing in parallel

Value

QTE Object

References

Athey, Susan and Guido Imbens. “Identification and Inference in Nonlinear Difference-in-Differences Models.” Econometrica 74.2, pp. 431-497, 2006.

Examples

## load the data
data(lalonde)

## Run the Quantile Difference in Differences method conditioning on
## age, education, black, hispanic, married, and nodegree
qd1 <- QDiD(re ~ treat, t=1978, tmin1=1975, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(qd1)

## load the data
data(lalonde)

## Run the Quantile Difference in Differences method conditioning on
## age, education, black, hispanic, married, and nodegree
qd1 <- QDiD(re ~ treat, t=1978, tmin1=1975, tname="year",
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(qd1)

qte: A package for computating quantile treatment effects

Description

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

Usage

QTE(
  qte,
  ate = NULL,
  qte.se = NULL,
  qte.lower = NULL,
  qte.upper = NULL,
  ate.se = NULL,
  ate.lower = NULL,
  ate.upper = NULL,
  c = NULL,
  alp = 0.05,
  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
)
QTE(
  qte,
  ate = NULL,
  qte.se = NULL,
  qte.lower = NULL,
  qte.upper = NULL,
  ate.se = NULL,
  ate.lower = NULL,
  ate.upper = NULL,
  c = NULL,
  alp = 0.05,
  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
`alp`	The significance level
`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

QTEparams

Description

QTEparams is an object that contains all the parameters passed to QTE methods

Usage

QTEparams(
  formla,
  xformla = NULL,
  t = NULL,
  tmin1 = NULL,
  tmin2 = NULL,
  tname = NULL,
  data,
  panel = FALSE,
  w = NULL,
  idname = NULL,
  probs,
  alp = NULL,
  method = NULL,
  plot = NULL,
  se = NULL,
  iters = NULL,
  retEachIter = NULL,
  bootstrapiter = NULL,
  seedvec = NULL,
  pl = NULL,
  cores = NULL
)
QTEparams(
  formla,
  xformla = NULL,
  t = NULL,
  tmin1 = NULL,
  tmin2 = NULL,
  tname = NULL,
  data,
  panel = FALSE,
  w = NULL,
  idname = NULL,
  probs,
  alp = NULL,
  method = NULL,
  plot = NULL,
  se = NULL,
  iters = NULL,
  retEachIter = NULL,
  bootstrapiter = NULL,
  seedvec = NULL,
  pl = NULL,
  cores = NULL
)

Arguments

`formla`	Should be some y on treatment variable
`xformla`	a formula for the other covariates such as ~ x1 + x2
`t`	The last period (not always used)
`tmin1`	The last pre-treatment period (not always used)
`tmin2`	The 2nd to last pre-treatment period (not always used)
`tname`	The name of the column containing time periods (not always used)
`data`	The name of the data frame
`panel`	Whether or not the data is panel
`w`	Additional (usually sampling) weights
`idname`	The name of the id column used with panel data (not always used)
`probs`	Which quantiles to produce quantile treatment effects for
`alp`	The significance level
`method`	The method to compute the propensity score
`plot`	boolean for whether or not to plot qtes
`se`	boolean whether or not to compute standard errors
`iters`	The number of bootstrap iterations to use to compute standard errors
`retEachIter`	boolean whether or not to return the full results from each bootstrap iteration
`bootstrapiter`	Used internally for determining whether or not a call is part of computing standard errors via the bootstrap
`seedvec`	A seed to compute the same bootstrap standard errors each time the method is called (not always used)
`pl`	Boolean for whether or not computing bootstrap standard errrors in parallel
`cores`	The number of cores to use if computing standard errors in in parallel

qtes2mat

Description

Turn multiple qtes into a matrix for printing

Usage

qtes2mat(qteList, sset = NULL, se = TRUE, rnd = 3)
qtes2mat(qteList, sset = NULL, se = TRUE, rnd = 3)

Arguments

`qteList`	a list of qte objects
`sset`	subset of qtes to keep
`se`	whether or not to include standard errors in the resulting matrix
`rnd`	how many disgits to round to

Value

matrix

diffQ

Description

takes a single set of quantiles

(not qtes as in diffquantiles) and returns the difference between particular ones

Usage

qteToTexreg(qteobj, tau = NULL, reportAte = T)
qteToTexreg(qteobj, tau = NULL, reportAte = T)

Arguments

`qteobj`	A QTE object
`tau`	Optional vector of taus to texreg results for
`reportAte`	Whether or not texreg the ATE (or ATT) as well

setupData

Description

setupData sets up the data to use in each compute method in the QTE package

Usage

setupData(qteParams)
setupData(qteParams)

Arguments

qteParams

object holding the function parameters

Value

qteData object holding data to be used in QTE functions

spatt

Description

spatt computes the Average Treatment Effect on the Treated (ATT) using the method of Abadie (2005)

Usage

spatt(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  w = NULL,
  panel = FALSE,
  idname = NULL,
  iters = 100,
  alp = 0.05,
  method = "logit",
  plot = FALSE,
  se = TRUE,
  retEachIter = FALSE,
  seedvec = NULL,
  pl = FALSE,
  cores = 2
)
spatt(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  w = NULL,
  panel = FALSE,
  idname = NULL,
  iters = 100,
  alp = 0.05,
  method = "logit",
  plot = FALSE,
  se = TRUE,
  retEachIter = FALSE,
  seedvec = NULL,
  pl = FALSE,
  cores = 2
)

Arguments

`formla`	The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary)
`xformla`	A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
`t`	The 3rd time period in the sample (this is the name of the column)
`tmin1`	The 2nd time period in the sample (this is the name of the column)
`tname`	The name of the column containing the time periods
`data`	The name of the data.frame that contains the data
`w`	an additional vector of sampling weights
`panel`	Boolean indicating whether the data is panel or repeated cross sections
`idname`	The individual (cross-sectional unit) id name
`iters`	The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
`alp`	The significance level used for constructing bootstrap confidence intervals
`method`	The method for estimating the propensity score when covariates are included
`plot`	Boolean whether or not the estimated QTET should be plotted
`se`	Boolean whether or not to compute standard errors
`retEachIter`	Boolean whether or not to return list of results from each iteration of the bootstrap procedure
`seedvec`	Optional value to set random seed; can possibly be used in conjunction with bootstrapping standard errors.
`pl`	boolean for whether or not to compute bootstrap error in parallel. Note that computing standard errors in parallel is a new feature and may not work at all on Windows.
`cores`	the number of cores to use if bootstrap standard errors are computed in parallel

Value

QTE object

References

Abadie (2005)

Examples

##load the data
data(lalonde)

## Run the panel.qtet method on the experimental data with no covariates
att1 <- spatt(re ~ treat, t=1978, tmin1=1975, tname="year",
 x=NULL, data=lalonde.psid.panel, idname="id", se=FALSE)
summary(att1)

## Run the panel.qtet method on the observational data with no covariates


##load the data
data(lalonde)

## Run the panel.qtet method on the experimental data with no covariates
att1 <- spatt(re ~ treat, t=1978, tmin1=1975, tname="year",
 x=NULL, data=lalonde.psid.panel, idname="id", se=FALSE)
summary(att1)

## Run the panel.qtet method on the observational data with no covariates

Summary of BoundsObj

Description

summary.BoundsObj is an object for holding bounds results

Usage

## S3 method for class 'BoundsObj'
summary(object, ...)
## S3 method for class 'BoundsObj'
summary(object, ...)

Arguments

`object`	A BoundsObj Object
`...`	Other params (for consistency as generic S3 method, but not used)

Value

summary.BoundsObj Object

Summary

Description

summary.QTE summarizes QTE objects

Usage

## S3 method for class 'QTE'
summary(object, ...)
## S3 method for class 'QTE'
summary(object, ...)

Arguments

`object`	A QTE Object
`...`	Other params (to work as generic method, but not used)

Package 'qte'

Help Index

bounds

Description

Usage

Arguments

Value

References

Examples

ci.qte

Description

Usage

Arguments

Value

References

Examples

ci.qtet

Description

Usage

Arguments

Value

References

Examples

Change in Changes

Description

Usage

Arguments

Value

References

Examples

cic_attgt

Description

Usage

Arguments

Value

cic2

Description

Usage

Arguments

compute.panel.qtet

Description

Usage

Arguments

Value

computeDiffSE

Description

Usage

Arguments

ddid2

Description

Usage

Arguments

Value

References

Examples

diffQ

Description

takes a single set of quantiles

Usage

Arguments

Value

DR

Description

Usage

Arguments

ggqte

Description

Usage

Arguments

Value

Lalonde (1986)'s NSW Dataset

Description

Usage

Format

References

Lalonde's Experimental Dataset

Description

Lalonde's Panel Experimental Dataset

Description

Lalonde's Observational Dataset