| 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
|
Value
civ returns an object of S3 class civ. An object of
class civ is a list containing the following components:
coefA vector of second-stage coefficient estimates.
iv_fitObject of class
ivregfrom the IV regression ofyonDandXusing the the estimated\hat{F}_Kas an instrument forD. See alsoAER::ivreg()for details.kcmeans_fitObject of class
kcmeansfrom the K-Conditional-Means regression ofDonZandX. See alsokcmeans::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 |
... |
Additional arguments passed to |
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
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)