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# Bootrstapped "Improved" Doubly Robust Difference-in-Differences with Repeated Cross Section data
# 2 periods and 2 groups
wboot_drdid_rc1 <- function(nn, n, y, post, D, int.cov, i.weights){
#-----------------------------------------------------------------------------
v <- stats::rexp(n)
#v <- v / mean(v)
#weights for the bootstrap
b.weights <- as.vector(i.weights * v)
# Propensity score estimation
# ps.b <- suppressWarnings(stats::glm(D ~ -1 + int.cov, family = "binomial", weights = b.weights)$fitted.values)
ps.b <- suppressWarnings(parglm::parglm(D ~ -1 + int.cov, family = "binomial", weights = b.weights)$fitted.values)
ps.b <- as.vector(ps.b)
ps.b <- pmin(ps.b, 1 - 1e-6)
#Compute the Outcome regression for the control group at the pre-treatment period, using ols.
# reg.coeff.pre.b <- stats::coef(stats::lm(y ~ -1 + int.cov,
# subset = ((D==0) & (post==0)),
# weights = b.weights))
control_pre <- (D == 0) & (post == 0)
reg.coeff.pre.b <- stats::coef(fastglm::fastglm(
x = int.cov[control_pre, , drop = FALSE],
y = y[control_pre],
weights = b.weights[control_pre],
family = gaussian(link = "identity")
))
out.y.pre.b <- as.vector(tcrossprod(reg.coeff.pre.b, int.cov))
#Compute the Outcome regression for the control group at the pre-treatment period, using ols.
# reg.coeff.post.b <- stats::coef(stats::lm(y ~ -1 + int.cov,
# subset = ((D==0) & (post==1)),
# weights = b.weights))
control_post <- (D == 0) & (post == 1)
reg.coeff.post.b <- stats::coef(fastglm::fastglm(
x = int.cov[control_post, , drop = FALSE],
y = y[control_post],
weights = b.weights[control_post],
family = gaussian(link = "identity")
))
out.y.post.b <- as.vector(tcrossprod(reg.coeff.post.b, int.cov))
# Combine the ORs
out.y.b <- post * out.y.post.b + (1 - post) * out.y.pre.b
# Compute AIPW estimator
att.b <- aipw_did_rc1(y, post, D, ps.b, out.y.b, b.weights)
#-----------------------------------------------------------------------------
return(att.b)
}
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