Cluster-robust variance-covariance matrix for an estimatr::iv_robust object.

vcovCR returns a sandwich estimate of the variance-covariance matrix of a set of regression coefficient estimates from an iv_robust object.

Usage

# S3 method for class 'iv_robust'
vcovCR(
  obj,
  cluster,
  type,
  target = NULL,
  inverse_var = FALSE,
  form = "sandwich",
  ...
)

Arguments

obj

Fitted model for which to calculate the variance-covariance matrix

cluster

Vector indicating which observations belong to the same cluster. Required for iv_robust objects unless the model was fitted with a clusters argument.

type

Character string specifying which small-sample adjustment should be used, with available options "CR0", "CR1", "CR1p", "CR1S", "CR2", or "CR3". See "Details" section of vcovCR for further information.

target

Optional matrix or vector describing the working variance-covariance model used to calculate the CR2 and CR4 adjustment matrices. If a vector, the target matrix is assumed to be diagonal. If not specified, the target is taken to be an identity matrix.

inverse_var

Not used for iv_robust objects.

form

Controls the form of the returned matrix. The default "sandwich" will return the sandwich variance-covariance matrix. Alternately, setting form = "meat" will return only the meat of the sandwich and setting form = B, where B is a matrix of appropriate dimension, will return the sandwich variance-covariance matrix calculated using B as the bread. form = "estfun" will return the (appropriately scaled) estimating function, the transposed crossproduct of which is equal to the sandwich variance-covariance matrix.

...

Additional arguments available for some classes of objects.

Value

An object of class c("vcovCR","clubSandwich"), which consists of a matrix of the estimated variance of and covariances between the regression coefficient estimates.

See also

vcovCR

Examples

if (requireNamespace("estimatr", quietly = TRUE)) withAutoprint({
  library(estimatr)

  set.seed(20261201)
  N <- 200
  cluster <- factor(rep(1:20, each = 10))
  z1 <- rnorm(N)
  z2 <- rnorm(N)
  x_exog <- rnorm(N)
  x_endog <- 0.5 * z1 + 0.3 * z2 + rnorm(N)
  y <- 1 + 2 * x_endog + 0.5 * x_exog + rnorm(N)
  dat <- data.frame(y, x_endog, x_exog, z1, z2, cluster)

  # Basic 2SLS with clusters and CR2
  iv_fit <- iv_robust(
    y ~ x_endog + x_exog | x_exog + z1 + z2,
    data = dat, 
    clusters = cluster, se_type = "CR2"
  )
  vcovCR(iv_fit)
  conf_int(iv_fit, vcov = "CR2")

  # 2SLS with fixed effects
  iv_fe <- iv_robust(
    y ~ x_endog + x_exog | x_exog + z1 + z2,
    fixed_effects = ~ cluster, 
    data = dat, 
    clusters = cluster,
    se_type = "CR2"
  )
  vcovCR(iv_fe)
  conf_int(iv_fe, vcov = "CR2")
})
#> > library(estimatr)
#> > set.seed(20261201)
#> > N <- 200
#> > cluster <- factor(rep(1:20, each = 10))
#> > z1 <- rnorm(N)
#> > z2 <- rnorm(N)
#> > x_exog <- rnorm(N)
#> > x_endog <- 0.5 * z1 + 0.3 * z2 + rnorm(N)
#> > y <- 1 + 2 * x_endog + 0.5 * x_exog + rnorm(N)
#> > dat <- data.frame(y, x_endog, x_exog, z1, z2, cluster)
#> > iv_fit <- iv_robust(y ~ x_endog + x_exog | x_exog + z1 + z2, data = dat, 
#> +     clusters = cluster, se_type = "CR2")
#> > vcovCR(iv_fit)
#>               (Intercept)      x_endog       x_exog
#> (Intercept)  0.0028574796 -0.001938895 0.0004459813
#> x_endog     -0.0019388949  0.012634349 0.0020154301
#> x_exog       0.0004459813  0.002015430 0.0059362835
#> > conf_int(iv_fit, vcov = "CR2")
#>        Coef. Estimate     SE d.f. Lower 95% CI Upper 95% CI
#>  (Intercept)    1.006 0.0535 18.8        0.894        1.118
#>      x_endog    2.126 0.1124 16.1        1.888        2.364
#>       x_exog    0.458 0.0770 14.3        0.293        0.623
#> > iv_fe <- iv_robust(y ~ x_endog + x_exog | x_exog + z1 + z2, fixed_effects = ~cluster, 
#> +     data = dat, clusters = cluster, se_type = "CR2")
#> > vcovCR(iv_fe)
#>             x_endog      x_exog
#> x_endog 0.013025078 0.002932599
#> x_exog  0.002932599 0.007977294
#> > conf_int(iv_fe, vcov = "CR2")
#>    Coef. Estimate     SE d.f. Lower 95% CI Upper 95% CI
#>  x_endog    2.115 0.1141 15.9         1.87        2.357
#>   x_exog    0.491 0.0893 14.1         0.30        0.683