Two-Stage Least Squares (2SLS) — morie_iv

Estimates a linear IV model via 2SLS (rmorie native k-class engine).

Usage

morie_iv_tsls(
  data,
  outcome,
  endogenous,
  instruments,
  exogenous = NULL,
  cluster = NULL,
  robust = TRUE,
  alpha = 0.05
)

Arguments

data: Data frame.
outcome: Name of the outcome column.
endogenous: Character vector of endogenous regressor names.
instruments: Character vector of excluded-instrument names.
exogenous: Optional character vector of exogenous covariate names.
cluster: Optional name of a cluster ID column. When given, standard errors are cluster-robust: the score is summed within each cluster before being squared, so within-cluster correlation is carried rather than assumed away. This selects the variance route; robust chooses between HC1 and conventional errors when no cluster is named, and details$se_type records which of the three produced the result.
robust: Logical; if TRUE use HC1 robust standard errors.
alpha: Significance level for confidence intervals.

Value

A list with class morie_iv_result containing coefficients, standard errors, t-statistics, p-values, confidence interval bounds, variable names, sample size, method label, and a details list.

Examples

set.seed(2)
n <- 1000
z <- rbinom(n, 1, 0.5); u <- rnorm(n)
d <- rbinom(n, 1, plogis(0.8 * z + 0.3 * u))
y <- 0.5 * d + 0.4 * u + rnorm(n, sd = 0.5)
df <- data.frame(y, d, z)
res <- morie_iv_tsls(df, "y", "d", "z")
res$coefficients["d"]
#>         d 
#> 0.6149994