ivDiag: Omnibus Function for IV Estimation and Diagnostics

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

Omnibus Function for IV Estimation and Diagnostics

Description

Conducts various estimation and diagnostic procedure for instrumental variable designs in one shot.

Usage

ivDiag(data, Y, D, Z, controls = NULL, FE = NULL, cl = NULL, weights = NULL,
  bootstrap = TRUE, run.AR = TRUE,
  nboots = 1000, parallel = TRUE, cores = NULL, 
  seed = 94305, prec = 4, debug = FALSE)

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.
bootstrap	whether to turn on bootstrap (TRUE by default).
run.AR	whether to run AR test (TRUE by default).
nboots	a numeric value indicating the number of bootstrap runs.
parallel	a logical flag controlling parallel computing.
cores	setting the number of cores.
prec	precision of CI in string (4 by default).
seed	setting seed.
debug	for debugging purposes.

Value

est_ols	results from an OLS regression.
est_2sls	results from a 2SLS regression.
AR	results from an Anderson-Rubin test
F_stat	various F statistics.
rho	Pearson correlation coefficient between the treatment and predicted treatment from the first stage regression (all covariates are partialled out).
tF	results from the tF procedure based on Lee et al. (2022)
est_rf	results from the reduced form regression.
est_fs	results from the first stage regression.
p_iv	the number of instruments.
N	the number of observations.
N_cl	the number of clusters.
df	the degree of freedom left from the 2SLS regression
nvalues	the unique values the outcome Y, the treatment D, and each instrument in Z in the 2SLS regression.

Author(s)

Apoorva Lal; Yiqing Xu

References

Lal, Apoorva, Mackenzie William Lockhart, Yiqing Xu, and Ziwen Zu. 2023. "How Much Should We Trust Instrumental Variable Estimates in Political Science? Practical Advice Based on 67 Replicated Studies." Available at: https://yiqingxu.org/papers/english/2021_iv/LLXZ.pdf

Lee, David S, Justin McCrary, Marcelo J Moreira, and Jack Porter. 2022. "Valid t-Ratio Inference for IV." American Economic Review 112 (10): 3260–90.

See Also

plot_coef eff_F AR_test tF

Examples

data(ivDiag)
g <- ivDiag(data = rueda, Y = "e_vote_buying", D = "lm_pob_mesa", 
    Z = "lz_pob_mesa_f", controls = c("lpopulation", "lpotencial"), 
    cl = "muni_code", bootstrap = FALSE, run.AR = FALSE)
plot_coef(g)
library(testthat)    
test_that("Check ivDiag output", {
  expect_equal(as.numeric(g$est_2sls[1,1]), -0.9835)
})

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