civ {civ}

Categorical Instrumental Variable Estimator.

Description

Implementation of the categorical instrumental variable estimator.

Usage

civ(y, D, Z, X = NULL, K = 2)

Arguments

y

The outcome variable, a numerical vector.

D

A matrix of endogenous variables.

Z

A matrix of instruments, where the first column corresponds to the categorical instrument.

X

An optional matrix of control variables.

K

The number of support points of the estimated instrument \hat{m}_K, an integer greater than 2.

Value

civ returns an object of S3 class civ. An object of class civ is a list containing the following components:

coef

A vector of second-stage coefficient estimates.

iv_fit

Object of class ivreg from the IV regression of y on D and X using the the estimated \hat{F}_K as an instrument for D. See also AER::ivreg() for details.

kcmeans_fit

Object of class kcmeans from the K-Conditional-Means regression of D on Z and X. See also kcmeans::kcmeans() for details.

K

Pass-through of selected user-provided arguments. See above.

References

Fox J, Kleiber C, Zeileis A (2023). "ivreg: Instrumental-Variables Regression by '2SLS', '2SM', or '2SMM', with Diagnostics". R package.

Wiemann T (2023). "Optimal Categorical Instruments."

Examples

# Simulate data from a simple IV model with 800 observations
nobs = 800 # sample size
Z <- sample(1:20, nobs, replace = TRUE) # observed instrument
Z0 <- Z %% 2 # underlying latent instrument
U_V <- matrix(rnorm(2 * nobs, 0, 1), nobs, 2) %*%
  chol(matrix(c(1, 0.6, 0.6, 1), 2, 2)) # first and second stage errors
D <- Z0 + U_V[, 2] # endogenous variable
y <- D + U_V[, 1] # outcome variable
# Estimate categorical instrument variable estimator with K = 2
civ_fit <- civ(y, D, Z, K = 3)
summary(civ_fit)