inst/tinytest/test_ols_internal.R

In s3alfisc/wildboottestjlr: Fast Wild Cluster Bootstrap Inference in R via WildBootTests.jl and JuliaConnectoR

# # test that methods for lm(), feols() and fixest produces equivalent results
#
# run <- TRUE
#
# if(run){
#   library(wildboottestjlr)
#   library(tinytest)
#   library(lfe)
#   library(fixest)
#
#   # ------------------------------------------------------------------ #
#   # test 1: default behavior
#   # - Tests .1 - one fixed effect,
#   # - Tests .2 - two fixed effects
#   # - Part A1: no fixed effect
#   #            - cluster vars in boottest
#   #            - cluster vars in feols and fixest
#   #            - in total: 5 boottest objects created
#   # - Part B1: one fixed effect
#   #            - cluster vars in boottest
#   #            - cluster vars in feols and fixest
#   #            - switch fe on and off
#   #            - in total: 10 boottest objects created
#   # - Part C1: two fixed effects
#   #            - cluster vars in boottest
#   #            - cluster vars in feols and fixest
#   #            - switch fe on and off
#   #            - in total: 10 boottest objects created
#   # ------------------------------------------------------------------ #
#
#   # library(wildboottestjlr)
#
#   # ---------------------------------------------------------------------------------------------- #
#   # Part 1: one cluster variable
#   # ---------------------------------------------------------------------------------------------- #
#
#   # ---------------------------------------------------------------------------------------------- #
#   # Part A1: no fixed effect in model
#
#   lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration ,
#                data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit <- feols(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit <- felm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration,
#                    data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit_c <- feols(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration,
#                        cluster = "group_id1",
#                        data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit_c <- felm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration | 0 | 0 | group_id1,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#
#   # test with floattype64
#   # pracma::tic()
#   boot_lm <-  suppressWarnings(boottest(object = lm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   # pracma::toc()
#
#   boot_fixest <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_felm <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest_c <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = TRUE, floattype = "Float64"))
#   boot_felm_c <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = TRUE, floattype = "Float64"))
#
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest, boot_felm, boot_fixest_c, boot_felm_c),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]]))
#          }
#   )
#
#   # ---------------------------------------------------------------------------------------------- #
#   # Part B1: one fixed effect in model
#
#   lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income + as.factor(Q1_immigration) ,
#                data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit <- feols(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit <- felm(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration,
#                    data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit_c <- feols(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration,
#                        cluster = "group_id1",
#                        data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit_c <- felm(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration  | 0 | group_id1,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#
#
#   boot_lm <-  suppressWarnings(boottest(object = lm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_felm <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest_c <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#   boot_felm_c <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#
#   boot_fixest_fe <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#   boot_felm_fe <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#   boot_fixest_c_fe <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#   boot_felm_c_fe <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#   boot_felm_c_fe1 <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = TRUE, floattype = "Float64"))
#
#   # no fixed effects: exactly equal
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest, boot_felm, boot_fixest_c, boot_felm_c),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]]))
#          }
#   )
#
#   # fixed effects: exactly equal
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_felm_fe, boot_fixest_c_fe, boot_felm_c_fe),
#                   function(x)
#                     expect_equivalent(boot_fixest_fe[[stat]], x[[stat]]))
#          }
#   )
#
#   # fixed effects: almost equal to boot_lm
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest_fe, boot_felm_fe, boot_fixest_c_fe, boot_felm_c_fe),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]], tol = 1e-02))
#          }
#   )
#
#
#   # ---------------------------------------------------------------------------------------------- #
#   # Part C1: two fixed effects in model
#
#   lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration + Q2_defense,
#                data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit <- feols(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration + Q2_defense,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit <- felm(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration + Q2_defense,
#                    data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit_c <- feols(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration + Q2_defense,
#                        cluster = "group_id1",
#                        data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit_c <- felm(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration + Q2_defense | 0 | group_id1,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#
#   boot_lm <-  suppressWarnings(boottest(object = lm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_felm <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest_c <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#   boot_felm_c <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#
#   boot_lm_fe <-  suppressWarnings(boottest(object = lm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest_fe <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#   boot_felm_fe <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#   boot_fixest_c_fe <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#   boot_felm_c_fe <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, fedfadj = FALSE, floattype = "Float64"))
#
#   # for(stat in c("point_estimate", "p_val", "t_stat", "conf_int")){
#   #   for(x in list(boot_fixest, boot_felm, boot_fixest_c, boot_felm_c)){
#   #     expect_equivalent(boot_lm[[stat]], x[[stat]])
#   #   }
#   # }
#
#   # no fixed effects: exactly equal
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest, boot_felm, boot_fixest_c, boot_felm_c),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]]))
#          }
#   )
#
#   # fixed effects: exactly equal
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_felm_fe, boot_fixest_c_fe, boot_felm_c_fe),
#                   function(x)
#                     expect_equivalent(boot_fixest_fe[[stat]], x[[stat]]))
#          }
#   )
#
#   # fixed effects: almost equal to boot_lm
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest_fe, boot_felm_fe, boot_fixest_c_fe, boot_felm_c_fe),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]], tol = 1e-02))
#          }
#   )
#
#
#
#   # ---------------------------------------------------------------------------------------------- #
#   # Part 2: two cluster variables
#   # ---------------------------------------------------------------------------------------------- #
#
#   # ---------------------------------------------------------------------------------------------- #
#   # Part A2: no fixed effect in model
#
#   lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration ,
#                data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit <- feols(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit <- felm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration,
#                    data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit_c <- feols(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration,
#                        cluster = "group_id1",
#                        data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit_c <- felm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration | 0 | 0 | group_id1,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#
#   boot_lm <-  suppressWarnings(boottest(object = lm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_felm <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest_c <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_felm_c <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest, boot_felm, boot_fixest_c, boot_felm_c),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]]))
#          }
#   )
#
#
#   # ---------------------------------------------------------------------------------------------- #
#   # Part B2: one fixed effect in model
#
#   lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration ,
#                data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit <- feols(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit <- felm(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration,
#                    data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit_c <- feols(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration,
#                        cluster = "group_id1",
#                        data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit_c <- felm(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration  | 0 | group_id1,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#
#   boot_lm <-  suppressWarnings(boottest(object = lm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64", fedfadj = FALSE))
#   boot_felm <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64", fedfadj = FALSE))
#   boot_fixest_c <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64", fedfadj = FALSE))
#   boot_felm_c <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64", fedfadj = FALSE))
#
#   boot_lm_fe <-  suppressWarnings(boottest(object = lm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64"))
#   boot_fixest_fe <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64", fedfadj = FALSE))
#   boot_felm_fe <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64", fedfadj = FALSE))
#   boot_fixest_c_fe <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64", fedfadj = FALSE))
#   boot_felm_c_fe <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE, floattype = "Float64", fedfadj = FALSE))
#
#   # no fixed effects: exactly equal
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest, boot_felm, boot_fixest_c, boot_felm_c),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]]))
#          }
#   )
#
#   # fixed effects: exactly equal
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_felm_fe, boot_fixest_c_fe, boot_felm_c_fe),
#                   function(x)
#                     expect_equivalent(boot_fixest_fe[[stat]], x[[stat]]))
#          }
#   )
#
#   # fixed effects: almost equal to boot_lm
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest_fe, boot_felm_fe, boot_fixest_c_fe, boot_felm_c_fe),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]], tol = 1e-02))
#          }
#   )
#
#
#
#
#   # ---------------------------------------------------------------------------------------------- #
#   # Part C2: two fixed effects in model
#
#   lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration + Q2_defense,
#                data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit <- feols(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration + Q2_defense,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit <- felm(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration + Q2_defense,
#                    data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   feols_fit_c <- feols(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration + Q2_defense,
#                        cluster = "group_id1",
#                        data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#   felm_fit_c <- felm(proposition_vote ~ treatment + ideology1 + log_income | Q1_immigration + Q2_defense | 0 | group_id1,
#                      data = wildboottestjlr:::create_data(N = 1000, N_G1 = 20, icc1 = 0.01, N_G2 = 10, icc2 = 0.01, numb_fe1 = 10, numb_fe2 = 10, seed = 1234))
#
#   boot_lm <-  suppressWarnings(boottest(object = lm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#   boot_fixest <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#   boot_felm <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#   boot_fixest_c <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#   boot_felm_c <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#
#   boot_lm_fe <-  suppressWarnings(boottest(object = lm_fit, clustid =  "group_id1", B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#   boot_fixest_fe <- suppressWarnings(boottest(object = feols_fit, clustid = c("group_id1"), fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#   boot_felm_fe <- suppressWarnings(boottest(object = felm_fit, clustid =  "group_id1", fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#   boot_fixest_c_fe <- suppressWarnings(boottest(object = feols_fit_c, clustid = c("group_id1"), fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#   boot_felm_c_fe <- suppressWarnings(boottest(object = felm_fit_c, clustid =  "group_id1", fe = "Q1_immigration", B = 999, rng = 911, param = "treatment", conf_int = TRUE))
#
#
#   # no fixed effects: exactly equal
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest, boot_felm, boot_fixest_c, boot_felm_c),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]], tol = 1e-02))
#          }
#   )
#
#   # fixed effects: exactly equal
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_felm_fe, boot_fixest_c_fe, boot_felm_c_fe),
#                   function(x)
#                     expect_equivalent(boot_fixest_fe[[stat]], x[[stat]]))
#          }
#   )
#
#   # fixed effects: almost equal to boot_lm
#   lapply(list("point_estimate", "p_val", "t_stat", "conf_int"),
#          function(stat){
#            lapply(list(boot_fixest_fe, boot_felm_fe, boot_fixest_c_fe, boot_felm_c_fe),
#                   function(x)
#                     expect_equivalent(boot_lm[[stat]], x[[stat]], tol = 1e-02))
#          }
#   )
#
#
# }