ivDiag {ivDiag}

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 first stage regression.

est_fs

results from the reduced form 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)
})