rdhte_lincom: RD Heterogeneous Treatment Effects. Linear combinations of...

In rdhte: Heterogeneous Treatment Effects in Regression Discontinuity Designs

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.

A detailed introduction to the software is given in Calonico, Cattaneo, Farrell, Palomba and Titiunik (2025b).

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), and Cattaneo and Titiunik (2022).

Usage

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

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 (percentage form, in [1, 100)); default is level = 95. Passing the fraction form (e.g. 0.95) is rejected with a clear error.
digits	Number of decimal places to format numeric outputs (default 3).

Value

A list with two data frames:

individual

One row per hypothesis. Columns: hypothesis, estimate (conventional point estimate of the linear combination), z_stat (asymptotic z-statistic from the bias-corrected fit), p_value (two-sided p-value from the standard normal), conf.low, conf.high (robust bias-corrected CI bounds at the requested confidence level).

joint

One row. Columns: statistic (Wald chi-squared from the bias-corrected fit), df (number of restrictions), p_value.

Numeric columns are rounded to digits decimal places.

Author(s)

Sebastian Calonico, University of California, Davis [e-mail].

Matias D. Cattaneo, Princeton University [e-mail].

Max H. Farrell, University of California, Santa Barbara [e-mail].

Filippo Palomba, Princeton University [e-mail].

Rocio Titiunik, Princeton University [e-mail].

References

Calonico, Cattaneo, Farrell, Palomba and Titiunik (2025): rdhte: 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.

Cattaneo and Titiunik. 2022. Regression Discontinuity Designs. Annual Review of Economics, 14: 821-851.

Calonico, Cattaneo, and Farrell. 2020. Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs. Econometrics Journal, 23(2): 192-210.

Calonico, Cattaneo, Farrell, and Titiunik. 2019. Regression Discontinuity Designs using Covariates. Review of Economics and Statistics, 101(3): 442-451.

Calonico, Cattaneo, and Titiunik. 2014a. Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica 82(6): 2295-2326.

Granzier, Pons, and Tricaud. 2023. Coordination and Bandwagon Effects: How Past Rankings Shape the Behavior of Voters and Candidates. American Economic Journal: Applied Economics, 15(4): 177-217.

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)

## Not run: 
data(rdhte_dataset)
with(rdhte_dataset, {
  rd_ideology <- rdhte(y = y, x = x, covs.hte = factor(w_ideology),
                       cluster = cluster_var)
  rdhte_lincom(rd_ideology,
               linfct = c("`factor(w_ideology)4` - `factor(w_ideology)3` = 0",
                          "`factor(w_ideology)4` = 0"))
})

## End(Not run)

rdhte documentation built on May 27, 2026, 1:06 a.m.