# OLS --------------------------------------------------------------------- test_that("correctly creates highest order shiftvalues", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 11 outputs <- suppressWarnings( EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = - 1, cluster = TRUE, anticipation_effects_normalization = TRUE) ) shiftvalues <- outputs$output$term largest_fd_lag <- as.double(stringr::str_extract(shiftvalues, "(?<=fd_lag)[0-9]+")) largest_fd_lead <- as.double(stringr::str_extract(shiftvalues, "(?<=fd_lead)[0-9]+")) largest_lag <- as.double(stringr::str_extract(shiftvalues, "(?<=lag)[0-9]+")) largest_lead <- as.double(stringr::str_extract(shiftvalues, "(?<=lead)[0-9]+")) expect_equal(max(largest_fd_lag, na.rm = TRUE), post + overidpost - 1) expect_equal(max(largest_fd_lead, na.rm = TRUE), pre + overidpre) expect_equal(max(largest_lag, na.rm = TRUE), post + overidpost) expect_equal(max(largest_lead, na.rm = TRUE), pre + overidpre) }) test_that("correctly throws an error when normalized coefficient is outside event-study window", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 7 normalize <- 15 expect_error(EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE)) }) test_that("throws an error when post + pre + overidpre + overidpost exceeds the data window", { post <- 10 pre <- 15 overidpre <- 20 overidpost <- 25 normalize <- 2 expect_error(EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE)) }) test_that("removes the correct column when normalize < 0", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 7 normalize <- -2 outputs <- EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_fd_lead", (-1 * normalize)) expect_equal(stringr::str_extract(normalization_column, "lead"), "lead") expect_true(!normalization_column %in% shiftvalues) expect_true(-1 * normalize > 0) }) test_that("removes the correct column when normalize = 0", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 7 normalize <- 0 outputs <- EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_fd") expect_equal(stringr::str_extract(normalization_column, "fd"), "fd") expect_true(!normalization_column %in% shiftvalues) expect_true(normalize == 0) }) test_that("does not create a first differenced variable when post, overidpost, pre, overidpre are all zero", { post <- 0 pre <- 0 overidpre <- 0 overidpost <- 0 normalize <- -1 outputs <- EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term expect_true(! "z_fd" %in% shiftvalues) }) test_that("tests that package and STATA output agree when post, overidpost, pre, overidpre are zero", { post <- 0 pre <- 0 overidpre <- 0 overidpost <- 0 normalize <- -1 outputs <- EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) coef_package <- outputs$output$coefficients[[1]] std_package <- outputs$output$std.error[[1]] STATA_output <- read.csv('./input/df_test_base_STATA_allzero.csv') coef_STATA <- STATA_output$coef[[1]] std_STATA <- STATA_output$std_error[[1]] epsilon <- 10e-7 expect_equal(coef_package, coef_STATA, tolerance = epsilon) expect_equal(std_package, std_STATA, tolerance = epsilon) }) test_that("does not create shiftvalues of differenced variable when post + overidpost - 1 < 1", { post <- 1 pre <- 0 overidpre <- 0 overidpost <- 0 normalize <- -1 outputs <- EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term n_true <- sum(grepl("fd_shiftvalues", shiftvalues)) expect_equal(n_true, 0) }) test_that("does not create leads of differenced variable when pre + overidpre < 1", { post <- 1 pre <- 0 overidpre <- 0 overidpost <- 0 normalize <- -1 outputs <- EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term n_true <- sum(grepl("fd_leads", shiftvalues)) expect_equal(n_true, 0) }) test_that("removes the correct column when normalize > 0", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 7 normalize <- 2 outputs <- EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_fd_lag", normalize) expect_equal(stringr::str_extract(normalization_column, "lag"), "lag") expect_true(!normalization_column %in% shiftvalues) expect_true(normalize > 0) }) test_that("removes the correct column when normalize = - (pre + overidpre + 1)", { post <- 3 pre <- 2 overidpre <- 1 overidpost <- 4 normalize <- -4 outputs <- EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_lead", -1 * (normalize + 1)) expect_equal(stringr::str_extract(normalization_column, "lead"), "lead") expect_true(!normalization_column %in% shiftvalues) }) test_that("removes the correct column when normalize = post + overidpost", { post <- 3 pre <- 2 overidpre <- 1 overidpost <- 4 normalize <- 5 outputs <- EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_lag", normalize) expect_equal(stringr::str_extract(normalization_column, "lag"), "lag") expect_true(!normalization_column %in% shiftvalues) }) test_that("subtraction is peformed on the correct column", { post <- 1 pre <- 1 overidpre <- 2 overidpost <- 2 df_first_diff <- ComputeFirstDifferences(df = df_sample_static, idvar = "id", timevar = "t", diffvar = "z") num_fd_lag_periods <- post + overidpost - 1 num_fd_lead_periods <- pre + overidpre furthest_lag_period <- num_fd_lag_periods + 1 df_fd_leads <- ComputeShifts(df_first_diff, idvar = "id", timevar = "t", shiftvar = paste0("z", "_fd"), shiftvalues = -num_fd_lead_periods:-1) df_fd_leads_shifted <- ComputeShifts(df_fd_leads, idvar = "id", timevar = "t", shiftvar = paste0("z", "_fd"), shiftvalues = 1:num_fd_lag_periods) df_lag <- ComputeShifts(df_fd_leads_shifted, idvar = "id", timevar = "t", shiftvar = "z", shiftvalues = furthest_lag_period) df_lag_lead <- ComputeShifts(df_lag, idvar = "id", timevar = "t", shiftvar = "z", shiftvalues = -num_fd_lead_periods) col_subtract_1 <- paste0("z", "_lead", num_fd_lead_periods) df_shift_minus_1 <- 1 - df_lag_lead[col_subtract_1] num_equal <- sum(df_shift_minus_1[col_subtract_1] == 1 - df_lag_lead[col_subtract_1], na.rm = TRUE) num_na <- sum(is.na(df_shift_minus_1[col_subtract_1])) column_subtract_degree <- as.double(stringr::str_extract(col_subtract_1, "(?<=lead)[0-9]+")) expect_equal(num_equal + num_na, nrow(df_lag_lead)) expect_equal(column_subtract_degree, pre + overidpre) }) # FHS --------------------------------------------------------------------- test_that("correctly creates highest order leads and shiftvalues", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 11 outputs <- suppressWarnings( EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, proxy = "eta_m", post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = - 1, cluster = TRUE, anticipation_effects_normalization = TRUE) ) shiftvalues <- outputs$output$term largest_fd_lag <- as.double(stringr::str_extract(shiftvalues, "(?<=fd_lag)[0-9]+")) largest_fd_lead <- as.double(stringr::str_extract(shiftvalues, "(?<=fd_lead)[0-9]+")) largest_lag <- as.double(stringr::str_extract(shiftvalues, "(?<=lag)[0-9]+")) largest_lead <- as.double(stringr::str_extract(shiftvalues, "(?<=lead)[0-9]+")) expect_equal(max(largest_fd_lag, na.rm = TRUE), post + overidpost - 1) expect_equal(max(largest_fd_lead, na.rm = TRUE), pre + overidpre) expect_equal(max(largest_lag, na.rm = TRUE), post + overidpost) expect_equal(max(largest_lead, na.rm = TRUE), pre + overidpre) }) test_that("correctly throws an error when normalized coefficient is outside event-study window", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 7 normalize <- 15 expect_error(EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, proxy = "eta_m", post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE)) }) test_that("throws an error when post + pre + overidpre + overidpost exceeds the data window", { post <- 10 pre <- 15 overidpre <- 20 overidpost <- 25 normalize <- 2 expect_error(EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, proxy = "eta_m", post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE)) }) test_that("removes the correct column when normalize < 0", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 7 normalize <- -2 outputs <- EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, proxy = "eta_m", post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_fd_lead", (-1 * normalize)) expect_equal(stringr::str_extract(normalization_column, "lead"), "lead") expect_true(!normalization_column %in% shiftvalues) expect_true(-1 * normalize > 0) }) test_that("removes the correct column when normalize = 0", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 7 normalize <- 0 outputs <- EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, proxy = "eta_m", post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_fd") expect_equal(stringr::str_extract(normalization_column, "fd"), "fd") expect_true(!normalization_column %in% shiftvalues) expect_true(normalize == 0) }) test_that("FHS does not run when post, pre, overidpre, and overidpost are all 0", { post <- 0 pre <- 0 overidpre <- 0 overidpost <- 0 normalize <- -1 expect_error( outputs <- EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, proxy = "eta_m", post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) ) }) test_that("removes the correct column when normalize > 0", { post <- 2 pre <- 3 overidpre <- 4 overidpost <- 7 normalize <- 2 outputs <- EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, proxy = "eta_m", post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_fd_lag", normalize) expect_equal(stringr::str_extract(normalization_column, "lag"), "lag") expect_true(!normalization_column %in% shiftvalues) expect_true(normalize > 0) }) test_that("removes the correct column when normalize = - (pre + overidpre + 1)", { post <- 3 pre <- 2 overidpre <- 1 overidpost <- 4 normalize <- -4 outputs <- EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, proxy = "eta_m", post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_lead", -1 * (normalize + 1)) expect_equal(stringr::str_extract(normalization_column, "lead"), "lead") expect_true(!normalization_column %in% shiftvalues) }) test_that("removes the correct column when normalize = post + overidpost", { post <- 3 pre <- 2 overidpre <- 1 overidpost <- 4 normalize <- 5 outputs <- EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, "eta_m", post = post, pre = pre, overidpre = overidpre, overidpost = overidpost, normalize = normalize, cluster = TRUE, anticipation_effects_normalization = TRUE) shiftvalues <- outputs$output$term normalization_column <- paste0("z", "_lag", normalize) expect_equal(stringr::str_extract(normalization_column, "lag"), "lag") expect_true(!normalization_column %in% shiftvalues) }) test_that("proxyIV selection works", { expect_message( suppressWarnings( EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", proxy = "eta_m", FE = TRUE, TFE = TRUE, post = 2, overidpost = 2, pre = 1, overidpre = 2, normalize = -1, cluster = TRUE, anticipation_effects_normalization = TRUE) ), "Defaulting to strongest lead of differenced policy variable: proxyIV = z_fd_lead3. To specify a different proxyIV use the proxyIV argument." ) expect_message( suppressWarnings( EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", proxy = "eta_m", FE = TRUE, TFE = TRUE, post = 1, overidpost = 2, pre = 2, overidpre = 2, normalize = -1, cluster = TRUE, anticipation_effects_normalization = TRUE) ), "Defaulting to strongest lead of differenced policy variable: proxyIV = z_fd_lead4. To specify a different proxyIV use the proxyIV argument." ) expect_message( suppressWarnings( EventStudy(estimator = "FHS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", proxy = "eta_m", FE = TRUE, TFE = TRUE, post = 1, overidpost = 2, pre = 6, overidpre = 2, normalize = -1, cluster = TRUE, anticipation_effects_normalization = TRUE) ), "Defaulting to strongest lead of differenced policy variable: proxyIV = z_fd_lead5. To specify a different proxyIV use the proxyIV argument." ) }) test_that("warning with correct normalize and pre is thrown when anticpation effects are allowed and anticipation_effects_normalization is TRUE", { expect_warning( EventStudy(estimator = "OLS", data = example_data, outcomevar = "y_base", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", FE = TRUE, TFE = TRUE, post = 1, pre = 1, overidpre = 4, overidpost = 5, normalize = - 1, cluster = TRUE, anticipation_effects_normalization = TRUE), paste("You allowed for anticipation effects 1 periods before the event, so the coefficient at -2 was selected to be normalized to zero.", "To override this, change anticipation_effects_normalization to FALSE.") ) })