Inference Methods for the Categorical Instrumental Variable Estimator.

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

Usage

# S3 method for 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." https://arxiv.org/abs/2311.17021

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)
#> 
#> Call:
#> AER::ivreg(formula = y ~ D | m_hat)
#> 
#> Residuals:
#>       Min        1Q    Median        3Q       Max 
#> -2.755541 -0.590251 -0.005237  0.581280  3.469159 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -0.05130    0.04616  -1.111    0.267    
#> D            1.07359    0.06420  16.723   <2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.9307 on 798 degrees of freedom
#> Multiple R-Squared: 0.7347,	Adjusted R-squared: 0.7343 
#> Wald test: 279.7 on 1 and 798 DF,  p-value: < 2.2e-16 
#>