Continuously-Updated GMM (CUE-GMM)

Usage

morie_iv_cue_gmm(
  data,
  outcome,
  endogenous,
  instruments,
  exogenous = NULL,
  max_iter = 100,
  tol = 1e-08,
  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.
max_iter: Outer iteration cap (default 100).
tol: Convergence tolerance on the objective.
alpha: Significance level for confidence intervals.

Value

A named list with elements coefficients, std_errors, t_stats, p_values, ci_lower, ci_upper, variable_names, n_obs, method, details.

Examples

set.seed(1); n <- 400
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)
morie_iv_cue_gmm(df, "y", "d", "z")
#> $coefficients
#> (Intercept)           d 
#> -0.05005272  0.55823628 
#> 
#> $std_errors
#> (Intercept)           d 
#>   0.1704757   0.3074927 
#> 
#> $t_stats
#> (Intercept)           d 
#>  -0.2936062   1.8154455 
#> 
#> $p_values
#> (Intercept)           d 
#>  0.76905882  0.06945547 
#> 
#> $ci_lower
#> (Intercept)           d 
#> -0.38417890 -0.04443837 
#> 
#> $ci_upper
#> (Intercept)           d 
#>   0.2840735   1.1609109 
#> 
#> $variable_names
#> [1] "(Intercept)" "d"          
#> 
#> $n_obs
#> [1] 400
#> 
#> $method
#> [1] "cue-gmm (rmorie native)"
#> 
#> $details
#> $details$vcov
#>             (Intercept)           d
#> (Intercept)  0.02906196 -0.05139826
#> d           -0.05139826  0.09455177
#> 
#> $details$J
#> [1] 3.276406e-28
#> 
#> $details$converged
#> [1] TRUE
#> 
#>