tests/testthat/test-inference.R
In bcallaway11/did: Treatment Effects with Multiple Periods and Groups
#-----------------------------------------------------------------------------
#
# These are tests for the inference procedure for computing ATT(g,t)'s and
# different aggregation methods. These tests are meant to run fast ---
# they only confirm that standard errors are not changing across different
# iterations of the code. More extensive/direct tests are available at
# did/tests/att_gt_inference_tests.Rmd.
#
# IMPORTANT: For these tests to run, version 2.1.2 of the did package should
# be available.
#
# The following code will install version 2.1.2 of the did package into
# the directory above:
# install.packages("did", version="2.1.2", lib=old_did_path)
#
#-----------------------------------------------------------------------------
library(DRDID)
library(BMisc)
# library(ggplot2)
# library(ggpubr)
temp_lib <- tempfile()
dir.create(temp_lib)
remotes::install_version("did", version = "2.1.2", lib = temp_lib, repos = "http://cran.us.r-project.org")
test_that("inference with balanced panel data and aggregations", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
# tryCatch(detach("package:did"), error=function(e) "")
# library(did, lib.loc="~/R/old_packages/")
# first: make sure that we are using right version of package for these estimates
# expect_true(packageVersion("did") == "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr"
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg"
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw"
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr"
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg"
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw"
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .01)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .01)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .01)
# checks for aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw"
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg"
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr"
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .01)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .01)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .01)
})
test_that("inference with clustering", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster"
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster"
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster"
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster"
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster"
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster"
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .01)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .01)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .01)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster"
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster"
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster"
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .01)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .01)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .01)
})
test_that("same inference with unbalanced panel and panel data", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
res_factor <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster"
)
#-----------------------------------------------------------------------------
# clustered standard errors with unbalanced panel
res_ub <- att_gt(
yname = "Y",
tname = "period",
idname = "id",
gname = "G",
xformla = ~X,
data = data,
panel = TRUE,
allow_unbalanced_panel = TRUE,
clustervars = "cluster"
)
# check that influence function is the same if we use unbalanced panel approach
# vs. balanced panel approach
# Note: differences are showing up in never-treated units (that are towards end of sample)
expect_true(all(res_factor$inffunc == res_ub$inffunc))
})
test_that("inference with repeated cross sections", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp, panel = FALSE)
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = FALSE
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = FALSE
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = FALSE
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
# upping the tolerance because repeated cross sections
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .05)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .05)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .05)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = FALSE
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = FALSE
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = FALSE
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .05)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .05)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .05)
})
test_that("inference with repeated cross sections and clustering", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp, panel = FALSE)
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", panel = FALSE
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", panel = FALSE
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", panel = FALSE
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
# upping the tolerance here because of repeated cross sections & clustering
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .05)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .05)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .05)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", panel = FALSE
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", panel = FALSE
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", panel = FALSE
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .05)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .05)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .05)
})
test_that("inference with unbalanced panel", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
data <- data[-3, ]
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = TRUE, allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = TRUE, allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = TRUE, allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = TRUE, allow_unbalanced_panel = TRUE
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = TRUE, allow_unbalanced_panel = TRUE
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = TRUE, allow_unbalanced_panel = TRUE
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .01)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .01)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .01)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = TRUE, allow_unbalanced_panel = TRUE
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = TRUE, allow_unbalanced_panel = TRUE
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = TRUE, allow_unbalanced_panel = TRUE
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .01)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .01)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .01)
})
test_that("inference with unbalanced panel and clustering", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
data <- data[-3, ]
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
# ipw
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .01)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .01)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .01)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .01)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .01)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .01)
})
unlink(temp_lib)
bcallaway11/did documentation built on Nov. 15, 2024, 7:31 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#-----------------------------------------------------------------------------
#
# These are tests for the inference procedure for computing ATT(g,t)'s and
# different aggregation methods. These tests are meant to run fast ---
# they only confirm that standard errors are not changing across different
# iterations of the code. More extensive/direct tests are available at
# did/tests/att_gt_inference_tests.Rmd.
#
# IMPORTANT: For these tests to run, version 2.1.2 of the did package should
# be available.
#
# The following code will install version 2.1.2 of the did package into
# the directory above:
# install.packages("did", version="2.1.2", lib=old_did_path)
#
#-----------------------------------------------------------------------------
library(DRDID)
library(BMisc)
# library(ggplot2)
# library(ggpubr)
temp_lib <- tempfile()
dir.create(temp_lib)
remotes::install_version("did", version = "2.1.2", lib = temp_lib, repos = "http://cran.us.r-project.org")
test_that("inference with balanced panel data and aggregations", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
# tryCatch(detach("package:did"), error=function(e) "")
# library(did, lib.loc="~/R/old_packages/")
# first: make sure that we are using right version of package for these estimates
# expect_true(packageVersion("did") == "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr"
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg"
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw"
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr"
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg"
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw"
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .01)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .01)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .01)
# checks for aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw"
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg"
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr"
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .01)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .01)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .01)
})
test_that("inference with clustering", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster"
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster"
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster"
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster"
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster"
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster"
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .01)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .01)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .01)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster"
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster"
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster"
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .01)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .01)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .01)
})
test_that("same inference with unbalanced panel and panel data", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
res_factor <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster"
)
#-----------------------------------------------------------------------------
# clustered standard errors with unbalanced panel
res_ub <- att_gt(
yname = "Y",
tname = "period",
idname = "id",
gname = "G",
xformla = ~X,
data = data,
panel = TRUE,
allow_unbalanced_panel = TRUE,
clustervars = "cluster"
)
# check that influence function is the same if we use unbalanced panel approach
# vs. balanced panel approach
# Note: differences are showing up in never-treated units (that are towards end of sample)
expect_true(all(res_factor$inffunc == res_ub$inffunc))
})
test_that("inference with repeated cross sections", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp, panel = FALSE)
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = FALSE
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = FALSE
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = FALSE
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
# upping the tolerance because repeated cross sections
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .05)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .05)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .05)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = FALSE
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = FALSE
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = FALSE
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .05)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .05)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .05)
})
test_that("inference with repeated cross sections and clustering", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp, panel = FALSE)
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", panel = FALSE
)
},
args = list(data = data, temp_lib = temp_lib)
)
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", panel = FALSE
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", panel = FALSE
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", panel = FALSE
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
# upping the tolerance here because of repeated cross sections & clustering
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .05)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .05)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .05)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", panel = FALSE
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", panel = FALSE
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", panel = FALSE
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .05)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .05)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .05)
})
test_that("inference with unbalanced panel", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
data <- data[-3, ]
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = TRUE, allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = TRUE, allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = TRUE, allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = TRUE, allow_unbalanced_panel = TRUE
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = TRUE, allow_unbalanced_panel = TRUE
)
# reg
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = TRUE, allow_unbalanced_panel = TRUE
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .01)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .01)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .01)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", panel = TRUE, allow_unbalanced_panel = TRUE
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", panel = TRUE, allow_unbalanced_panel = TRUE
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", panel = TRUE, allow_unbalanced_panel = TRUE
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .01)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .01)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .01)
})
test_that("inference with unbalanced panel and clustering", {
sp <- did::reset.sim()
data <- did::build_sim_dataset(sp)
data <- data[-3, ]
set.seed(1234)
# dr
dr_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# reg
reg_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
# ipw
ipw_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
},
args = list(data = data, temp_lib = temp_lib)
)
expect_true(packageVersion("did") != "2.1.2", "wrong version of package")
set.seed(1234)
# dr
dr_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
# reg
reg_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
# ipw
ipw_new <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
# checks for ATT(g,t)'s
# check that the influence function is the same
expect_true(all(dr_new$inffunc == dr_2.1.2$inffunc))
expect_true(all(reg_new$inffunc == reg_2.1.2$inffunc))
expect_true(all(ipw_new$inffunc == ipw_2.1.2$inffunc))
# standard errors should be close
# not totally sure, but I think slight differences are expected
# perhaps from implementing the multiplier on the C++ side
# in newer versions of the code
expect_equal(dr_2.1.2$se[1], dr_new$se[1], tol = .01)
expect_equal(reg_2.1.2$se[1], reg_new$se[1], tol = .01)
expect_equal(ipw_2.1.2$se[1], ipw_new$se[1], tol = .01)
# aggregations
set.seed(1234)
dyn_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "ipw", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
aggte(res, type = "dynamic")
},
args = list(data = data, temp_lib = temp_lib)
)
group_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "reg", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
aggte(res, type = "group")
},
args = list(data = data, temp_lib = temp_lib)
)
cal_2.1.2 <- callr::r(
function(data, temp_lib) {
library(did, lib.loc = temp_lib)
res <- att_gt(
yname = "Y", xformla = ~X, data = data, tname = "period", idname = "id",
gname = "G", est_method = "dr", clustervars = "cluster", allow_unbalanced_panel = TRUE
)
aggte(res, type = "calendar")
},
args = list(data = data, temp_lib = temp_lib)
)
dyn_new <- aggte(ipw_new, type = "dynamic")
group_new <- aggte(reg_new, type = "group")
cal_new <- aggte(dr_new, type = "calendar")
# checks for aggregations
expect_true(all(dyn_2.1.2$inffunc == dyn_new$inffunc))
expect_true(all(group_2.1.2$inffunc == group_new$inffunc))
expect_true(all(cal_2.1.2$inffunc == cal_new$inffunc))
# standard errors for aggregations
expect_equal(dyn_2.1.2$se[1], dyn_new$se[1], tol = .01)
expect_equal(group_2.1.2$se[1], group_new$se[1], tol = .01)
expect_equal(cal_2.1.2$se[1], cal_new$se[1], tol = .01)
})
unlink(temp_lib)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.