Title: Categorical Instrumental Variables
Version: 0.1.0
Date: 2023-12-07
Description: Implementation of the categorical instrumental variable (CIV) estimator proposed by Wiemann (2023) <doi:10.48550/arXiv.2311.17021>. CIV allows for optimal instrumental variable estimation in settings with relatively few observations per category. To obtain valid inference in these challenging settings, CIV leverages a regularization assumption that implies existence of a latent categorical variable with fixed finite support achieving the same first stage fit as the observed instrument.
License: GPL (≥ 3)
URL: https://github.com/thomaswiemann/civ
BugReports: https://github.com/thomaswiemann/civ/issues
Encoding: UTF-8
RoxygenNote: 7.2.3
Depends: R (≥ 3.6)
Imports: stats, AER, kcmeans
Suggests: testthat (≥ 3.0.0), covr, knitr, rmarkdown
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2023-12-07 22:09:29 UTC; thomas
Author: Thomas Wiemann [aut, cre]
Maintainer: Thomas Wiemann <wiemann@uchicago.edu>
Repository: CRAN
Date/Publication: 2023-12-08 11:00:06 UTC

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)

Inference Methods for the Categorical Instrumental Variable Estimator.

Description

Inference methods for the categorical instrumental variable estimators. Simple wrapper for AER::summary.ivreg().

Usage

## S3 method for class 'civ'
summary(object, ...)

Arguments

object

An object of class civ as fitted by civ().

...

Additional arguments passed to summary.ivreg. See AER::summary.ivreg() for a complete list of arguments.

Value

An object of class summary.ivreg with inference results.

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."

See Also

AER::summary.ivreg()

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)