rdhte (version 0.1.0)

rdhte_lincom: RD Heterogeneous Treatment Effects. Linear combinations of parameters

Description

rdhte_lincom computes point estimates, p-values, and robust bias-corrected confidence intervals for linear combinations of parameters after any estimation using rdhte (Calonico, Cattaneo, Farrell, Palomba and Titiunik, 2025a). Inference is implemented using robust bias-correction methods (Calonico, Cattaneo, and Titiunik, 2014). It is based on the R function glht.

Companion commands: rdhte for estimation and inference of RD-HTE and rdbwhte for data-driven bandwidth selection.

Related software packages for analysis and interpretation of RD designs and related methods are available in: https://rdpackages.github.io/.

For background methodology, see Calonico, Cattaneo, Farrell, and Titiunik (2019), Calonico, Cattaneo and Farrell (2020), Cattaneo and Titiunik (2022).

Usage

rdhte_lincom(model, linfct, level = 95, digits = 3)

Value

A list with two data frames: 'individual' and 'joint', with rounded values.

Arguments

model

a fitted model returned by rdhte.

linfct

a specification of the linear hypotheses to be tested. Linear functions can be specified by either the matrix of coefficients or by symbolic descriptions of one or more linear hypotheses.

level

Confidence level for intervals; default is level = 95.

digits

Number of decimal places to format numeric outputs (default 3).

Author

Sebastian Calonico, University of California, Davis .

Matias D. Cattaneo, Princeton University .

Max H. Farrell, University of California, Santa Barbara .

Filippo Palomba, Princeton University .

Rocio Titiunik, Princeton University .

References

Calonico, Cattaneo, Farrell, Palomba and Titiunik (2025): rdhte: Learning Conditional Average Treatment Effects in RD Designs. Working paper.

Calonico, Cattaneo, Farrell, Palomba and Titiunik (2025): Treatment Effect Heterogeneity in Regression Discontinuity Designs. Working paper

See Also

rdhte, rdbwhte

Examples

set.seed(123)
n <- 1000
X <- runif(n, -1, 1)
W <- rbinom(n, 1, 0.5)
Y <- 3 + 2*X + 1.5*X^2 + 0.5*X^3 + sin(2*X) + 3*W*(X>=0) + rnorm(n)
m1 <- rdhte(y = Y, x = X, covs.hte = factor(W))
linfct <- c("`factor(W)0` - `factor(W)1` = 0")
rdhte_lincom(model = m1, linfct = linfct)