AR_test: Anderson Rubin Test

In apoorvalal/ivDiag: Estimation and Diagnostic Tools for Instrumental Variables Designs

Anderson Rubin Test

Description

Performs the Anderson Rubin test, which is robust to weak instruments.

Usage

AR_test(data, Y, D, Z, controls, FE = NULL, cl = NULL, weights = NULL, 
  prec = 4, CI = TRUE, alpha = 0.05, parallel = NULL, cores = NULL)

Arguments

data	name of a dataframe.
Y	a string indicating the outcome variable.
D	a string indicating the treatment variable.
Z	a vector of strings indicating the instrumental variables.
controls	a vector of strings indicating the control variables.
FE	a vector of strings indicating the fixed effects variables.
cl	a string indicating the clustering variable.
weights	a string indicating the variable that stores weights.
CI	a logical flag controlling whether to calcualte the confidence interval using the inversion method.
prec	precision of results (4 by default).
alpha	level of statitical significance; the default is 0.05.
parallel	a logical flag controlling parallel computing.
cores	setting the number of cores.

Value

Fstat	F statistic, degrees of freedoms, and p-value.
ci.print	Confidence interval via intervsion (printed version).
ci	Confidence interval via intervsion (numeric version).
bounded	If the confidence interval is bounded.

References

Chernozhukov, Victor, and Christian Hansen. 2008. "The Reduced Form: A Simple Approach to Inference with Weak Instruments." Economics Letters 100 (1): 68–71.

See Also

ivDiag

Examples

data(ivDiag)
AR.out <- AR_test(data = rueda, Y = "e_vote_buying", D = "lm_pob_mesa", 
    Z = "lz_pob_mesa_f", controls = c("lpopulation", "lpotencial"),
    cl = "muni_code", CI = FALSE)
library(testthat)    
test_that("Check AR results", {
  expect_equal(as.numeric(AR.out$Fstat[1]), 48.4768)
})

apoorvalal/ivDiag documentation built on July 12, 2024, 12:53 p.m.