pstest (version 0.1.3.900)

pstest: pstest: Tests for the Propensity Score

Description

pstest computes Kolmogorov-Smirnov and Cramer-von Mises type tests for the null hypothesis that a parametric model for the propensity score is is correctly specified. For details of the testing procedure, see Sant'Anna and Song (2016),'Specification Tests for the Propensity Score'.

Usage

pstest(d, pscore, xpscore, pscore.model = NULL, model = "logit",
  w = "ind", dist = "Mammen", nboot = 1000, cores = 1,
  chunk = 1000)

Arguments

d

a vector containing the binary treatment indicator.

pscore

a vector containing the estimated propensity scores.

xpscore

a matrix (or data frame) containing the covariates (and their transformations) included in the propensity score estimation. It should also include the constant term.

pscore.model

in case you you set model="het.probit", pscore.model is the entire hetglm object. Default for pscore.model is NULL.

model

a description of the functional form (link function) used to estimated propensity score. The alternatives are: 'logit' (default), 'probit', and het.probit

w

a description of which weight function the projection is based on. The alternatives are 'ind' (default), which set \(w(q,u)=1(q<=u)\), 'exp', which set \(w(q,u)=exp(qu)\), 'logistic', which set \(w(q,u)=1/[1+exp(1-qu)]\), 'sin', which set \(w(q,u)=sin(qu)\), and 'sincos', which set \(w(q,u)=sin(qu)+cos(qu)\).

dist

a description of which distribution to use during the bootstrap. The alternatives are 'Mammen' (default), and 'Rademacher'.

nboot

number of bootstrap replicates to perform. Default is 1,000.

cores

number of cores to use during the bootstrap. Default is 1. If cores is greater than 1, the bootstrap is conducted using parLapply, instead of lapply type call.

chunk

a value that determine the size of each 'tile'. Such argument is used to split the original data into chunks, saving memory. Default value is 1,000. If the pstest function throw a memory error, you should choose a smaller value for chunk.

Value

a list containing the Kolmogorov-Smirnov and Cramer-von Mises test statistics for the null hypothesis of correctly specified propensity score model (kstest and cvmtest, respectively), and their associate bootstrapped p-values, pvks and pvcvm, respectively. All inputs are also returned.

References

Sant'Anna, Pedro H. C, and Song, Xiaojun (2019), Specification Tests for the Propensity Score, Journal of Econometrics, vol. 210 (2), p. 379-404.

Examples

# NOT RUN {
# Example based on simulation data
# Simulate vector of covariates
set.seed(1234)
x1 <- runif(100)
x2 <- rt(100, 5)
x3 <- rpois(100, 3)
# generate treatment status score based on Probit Specification
treat <- (x1 + x2 + x3 >= rnorm(100, 4, 5))
# estimate correctly specified propensity score based on Probit
pscore <- stats::glm(treat ~ x1 + x2 + x3, family = binomial(link = "probit"),
              x = TRUE)
# Test the correct specification of estimated propensity score, using
# the weight function 'ind', and bootstrap based on 'Mammen'.
pstest(d = pscore$y, pscore = pscore$fit, xpscore = pscore$x,
       model = "probit", w = "ind", dist = "Mammen")
# Alternatively, one can use the 'sin' weight function
pstest(d = pscore$y, pscore = pscore$fit, xpscore = pscore$x,
       model = "probit", w = "sin", dist = "Mammen")

# }