rdhte_lincom {rdhte} | R Documentation |
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)
Arguments
model |
a fitted model returned by |
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 |
digits |
Number of decimal places to format numeric outputs (default 3). |
Value
A list with two data frames: 'individual' and 'joint', with rounded values.
Author(s)
Sebastian Calonico, University of California, Davis scalonico@ucdavis.edu.
Matias D. Cattaneo, Princeton University cattaneo@princeton.edu.
Max H. Farrell, University of California, Santa Barbara maxhfarrell@ucsb.edu.
Filippo Palomba, Princeton University fpalomba@princeton.edu.
Rocio Titiunik, Princeton University titiunik@princeton.edu.
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
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)