Omnibus Function for IV Estimation and Diagnostics

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

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.

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)
})
#> Test passed 🥳