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inst/doc/TWFE.R
In did: Treatment Effects with Multiple Periods and Groups
## ----setup2, include = FALSE--------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
echo = TRUE,
eval = FALSE
)
## ----DGP_viz, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # Load libraries and set baseline parameters
# library(tidyverse)
# library(lfe)
# library(fastDummies)
# library(ggthemes)
# library(did)
# theme_set(theme_clean() + theme(plot.background = element_blank()))
# #----------------------------------------------------------------------------
# iseed = 20201221
# nrep <- 100
# true_mu <- 1
# set.seed(iseed)
#
# #----------------------------------------------------------------------------
# ## Generate data - treated cohorts consist of 250 obs each, with the treatment effect still = true_mu on average
# make_data <- function(nobs = 1000,
# nstates = 40) {
#
# # unit fixed effects (unobservd heterogeneity)
# unit <- tibble(
# unit = 1:nobs,
# # generate state
# state = sample(1:nstates, nobs, replace = TRUE),
# unit_fe = rnorm(nobs, state/5, 1),
# # generate instantaneous treatment effect
# #mu = rnorm(nobs, true_mu, 0.2)
# mu = true_mu
# )
#
# # year fixed effects (first part)
# year <- tibble(
# year = 1980:2010,
# year_fe = rnorm(length(year), 0, 1)
# )
#
# # Put the states into treatment groups
# treat_taus <- tibble(
# # sample the states randomly
# state = sample(1:nstates, nstates, replace = FALSE),
# # place the randomly sampled states into four treatment groups G_g
# cohort_year = sort(rep(c(1986, 1992, 1998, 2004), 10))
# )
#
# # make main dataset
# # full interaction of unit X year
# expand_grid(unit = 1:nobs, year = 1980:2010) %>%
# left_join(., unit) %>%
# left_join(., year) %>%
# left_join(., treat_taus) %>%
# # make error term and get treatment indicators and treatment effects
# # Also get cohort specific trends (modify time FE)
# mutate(error = rnorm(nobs*31, 0, 1),
# treat = ifelse(year >= cohort_year, 1, 0),
# tau = ifelse(treat == 1, mu, 0),
# year_fe = year_fe + 0.1*(year - cohort_year)
# ) %>%
# # calculate cumulative treatment effects
# group_by(unit) %>%
# mutate(tau_cum = cumsum(tau)) %>%
# ungroup() %>%
# # calculate the dep variable
# mutate(dep_var = (2010 - cohort_year) + unit_fe + year_fe + tau_cum + error)
#
# }
# #----------------------------------------------------------------------------
# # make data
# data <- make_data()
#
# # plot
# plot1 <- data %>%
# ggplot(aes(x = year, y = dep_var, group = unit)) +
# geom_line(alpha = 1/8, color = "grey") +
# geom_line(data = data %>%
# group_by(cohort_year, year) %>%
# summarize(dep_var = mean(dep_var)),
# aes(x = year, y = dep_var, group = factor(cohort_year),
# color = factor(cohort_year)),
# size = 2) +
# labs(x = "", y = "Value", color = "Treatment group ") +
# geom_vline(xintercept = 1986, color = '#E41A1C', size = 2) +
# geom_vline(xintercept = 1992, color = '#377EB8', size = 2) +
# geom_vline(xintercept = 1998, color = '#4DAF4A', size = 2) +
# geom_vline(xintercept = 2004, color = '#984EA3', size = 2) +
# scale_color_brewer(palette = 'Set1') +
# theme(legend.position = 'bottom',
# #legend.title = element_blank(),
# axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# ggtitle("One draw of the DGP with homogeneous effects across cohorts \n and with all groups being eventually treated")+
# theme(plot.title = element_text(hjust = 0.5, size=12))
#
# plot1
#
# #ggsave("plot_dgp1.png", plot1, width = 10, height = 5, dpi = 100)
## ----ES1, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# # variables we will use
# keepvars <- c("`rel_year_-5`", "`rel_year_-4`", "`rel_year_-3`", "`rel_year_-2`",
# "rel_year_0", "rel_year_1", "rel_year_2", "rel_year_3", "rel_year_4", "rel_year_5")
#
# run_ES_DiD <- function(...) {
#
# # resimulate the data
# data <- make_data()
#
# # make dummy columns
# data <- data %>%
# # make dummies
# mutate(rel_year = year - cohort_year) %>%
# dummy_cols(select_columns = "rel_year") %>%
# # generate pre and post dummies
# mutate(Pre = ifelse(rel_year < -5, 1, 0),
# Post = ifelse(rel_year > 5, 1, 0))
#
# # estimate the model
# mod <- lfe::felm(dep_var ~ Pre + `rel_year_-5` + `rel_year_-4` + `rel_year_-3` + `rel_year_-2` +
# `rel_year_0` + `rel_year_1` + `rel_year_2` + `rel_year_3` + `rel_year_4` +
# `rel_year_5` + Post | unit + year | 0 | state, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_classical <- map_dfr(1:nrep, run_ES_DiD)
#
# colors <- c("True Effect" = "red", "Estimated Effect" = "blue")
#
# ES_plot_classical <- data_classical %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# ggtitle("TWFE event-study regression with binned end-points")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_classical
#
# #ggsave("es_plot_classical.png", ES_plot_classical, width = 10, height = 5, dpi = 150)
## ----ES3, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_ES_DiD_sat <- function(...) {
#
# # resimulate the data
# data <- make_data()
#
# # make dummy columns
# data <- data %>%
# # make relative year indicator
# mutate(rel_year = year - cohort_year)
#
# # get the minimum relative year - we need this to reindex
# min_year <- min(data$rel_year)
#
# # reindex the relative years
# data <- data %>%
# mutate(rel_year = rel_year - min_year) %>%
# dummy_cols(select_columns = "rel_year")
#
# # make regression formula
# indics <- paste("rel_year", (1:max(data$rel_year))[-(-1 - min_year)], sep = "_", collapse = " + ")
# keepvars <- paste("rel_year", c(-5:-2, 0:5) - min_year, sep = "_")
# formula <- as.formula(paste("dep_var ~", indics, "| unit + year | 0 | state"))
#
# # run mod
# mod <- felm(formula, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_sat <- map_dfr(1:nrep, run_ES_DiD_sat)
#
# ES_plot_sat <- data_sat %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# ggtitle("TWFE event-study regression with 'all' leads and lags")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
#
# ES_plot_sat
#
# #ggsave("es_plot_sat.png", ES_plot_sat, width = 10, height = 5, dpi = 150)
#
## ----CS, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_CS <- function(...) {
#
# # resimulate the data
# data <- make_data()
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "notyettreated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS <- map_dfr(1:nrep, run_CS)
#
# ES_plot_CS <- data_CS %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Not-yet-treated")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS
#
# #ggsave("es_plot_CS.png", ES_plot_CS, width = 10, height = 5, dpi = 150)
#
## ----DGP_viz2, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
#
# ## Generate data - treated cohorts consist of 250 obs each, with the treatment effect still = true_mu on average
# make_data2 <- function(nobs = 1000,
# nstates = 40) {
#
# # unit fixed effects (unobservd heterogeneity)
# unit <- tibble(
# unit = 1:nobs,
# # generate state
# state = sample(1:nstates, nobs, replace = TRUE),
# unit_fe = rnorm(nobs, state/5, 1),
# # generate instantaneous treatment effect
# #mu = rnorm(nobs, true_mu, 0.2)
# mu = true_mu
# )
#
# # year fixed effects (first part)
# year <- tibble(
# year = 1980:2010,
# year_fe = rnorm(length(year), 0, 1)
# )
#
# # Put the states into treatment groups
# treat_taus <- tibble(
# # sample the states randomly
# state = sample(1:nstates, nstates, replace = FALSE),
# # place the randomly sampled states into 1\{t \ge g \}G_g
# cohort_year = sort(rep(c(1986, 1992, 1998, 2004), 10))
# )
#
# # make main dataset
# # full interaction of unit X year
# expand_grid(unit = 1:nobs, year = 1980:2010) %>%
# left_join(., unit) %>%
# left_join(., year) %>%
# left_join(., treat_taus) %>%
# # make error term and get treatment indicators and treatment effects
# # Also get cohort specific trends (modify time FE)
# mutate(error = rnorm(nobs*31, 0, 1),
# treat = ifelse((year >= cohort_year)* (cohort_year != 2004), 1, 0),
# tau = ifelse(treat == 1, mu, 0),
# year_fe = year_fe + 0.1*(year - cohort_year)
# ) %>%
# # calculate cumulative treatment effects
# group_by(unit) %>%
# mutate(tau_cum = cumsum(tau)) %>%
# ungroup() %>%
# # calculate the dep variable
# mutate(dep_var = (2010 - cohort_year) + unit_fe + year_fe + tau_cum + error) %>%
# # Relabel 2004 cohort as never-treated
# mutate(cohort_year = ifelse(cohort_year == 2004, Inf, cohort_year))
#
# }
# #----------------------------------------------------------------------------
# # make data
# data <- make_data2()
#
# # plot
# plot2 <- data %>%
# ggplot(aes(x = year, y = dep_var, group = unit)) +
# geom_line(alpha = 1/8, color = "grey") +
# geom_line(data = data %>%
# group_by(cohort_year, year) %>%
# summarize(dep_var = mean(dep_var)),
# aes(x = year, y = dep_var, group = factor(cohort_year),
# color = factor(cohort_year)),
# size = 2) +
# labs(x = "", y = "Value", color = "Treatment group ") +
# geom_vline(xintercept = 1986, color = '#E41A1C', size = 2) +
# geom_vline(xintercept = 1992, color = '#377EB8', size = 2) +
# geom_vline(xintercept = 1998, color = '#4DAF4A', size = 2) +
# #geom_vline(xintercept = 2004, color = '#984EA3', size = 2) +
# scale_color_brewer(palette = 'Set1') +
# theme(legend.position = 'bottom',
# #legend.title = element_blank(),
# axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# scale_color_manual(labels = c("1986", "1992", "1998", "Never-treated"),
# values = c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3"))+
# ggtitle("One draw of the DGP with homogeneous effects across cohorts \n and with a never-treated group")+
# theme(plot.title = element_text(hjust = 0.5, size=12))
#
# plot2
#
# #ggsave("plot_dgp_never_treated.png",plot2, width = 10, height = 5, dpi = 100)
## ----ES1_never, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# # variables we will use
# keepvars <- c("`rel_year_-5`", "`rel_year_-4`", "`rel_year_-3`", "`rel_year_-2`",
# "rel_year_0", "rel_year_1", "rel_year_2", "rel_year_3", "rel_year_4", "rel_year_5")
#
# run_ES_DiD_never <- function(...) {
#
# # resimulate the data
# data <- make_data2()
# # make dummy columns
# data <- data %>%
# # make dummies
# mutate(rel_year = year - cohort_year) %>%
# mutate(rel_year = ifelse(rel_year == -Inf, NA, rel_year))%>%
# dummy_cols(select_columns = "rel_year") %>%
# mutate(across(starts_with("rel_year_"), ~replace_na(., 0))) %>%
# # generate pre and post dummies
# mutate(Pre = ifelse((rel_year < -5) * (!is.na(rel_year)), 1, 0),
# Post = ifelse((rel_year > 5) * (!is.na(rel_year)), 1, 0)) %>%
# mutate(Pre = ifelse(is.na(Pre), 0, Pre),
# Post = ifelse(is.na(Post), 0, Post))
#
# # estimate the model
# mod <- lfe::felm(dep_var ~ Pre + `rel_year_-5` + `rel_year_-4` + `rel_year_-3` + `rel_year_-2` +
# `rel_year_0` + `rel_year_1` + `rel_year_2` + `rel_year_3` + `rel_year_4` +
# `rel_year_5` + Post | unit + year | 0 | state, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_classical_never <- map_dfr(1:nrep, run_ES_DiD_never)
#
# ES_plot_classical_never <- data_classical_never %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("TWFE event-study regression with binned end-points")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_classical_never
#
# #ggsave("es_plot_classical_never.png", ES_plot_classical_never, width = 10, height = 5, dpi = 150)
## ----ES3_never, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_ES_DiD_sat_never <- function(...) {
#
# # resimulate the data
# data <- make_data2()
#
# # make dummy columns
# data <- data %>%
# # make relative year indicator
# mutate(rel_year = year - cohort_year)
#
# # get the minimum relative year - we need this to reindex
# min_year <- min(data$rel_year * (data$rel_year != -Inf), na.rm = T)
#
# # reindex the relative years
# data <- data %>%
# mutate(rel_year2 = rel_year) %>%
# mutate(rel_year = rel_year - min_year) %>%
# dummy_cols(select_columns = "rel_year") %>%
# select(-("rel_year_-Inf"))
#
#
# # make regression formula
# indics <- paste("rel_year", (1:max(data$rel_year))[-(-1 - min_year)], sep = "_", collapse = " + ")
# keepvars <- paste("rel_year", c(-5:-2, 0:5) - min_year, sep = "_")
# formula <- as.formula(paste("dep_var ~", indics, "| unit + year | 0 | state"))
#
# # run mod
# mod <- felm(formula, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_sat_never <- map_dfr(1:nrep, run_ES_DiD_sat_never)
#
# ES_plot_sat_never <- data_sat_never %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("TWFE event-study regression with 'all' leads and lags")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_sat_never
#
# #ggsave("es_plot_sat_never.png", ES_plot_sat_never, width = 10, height = 5, dpi = 150)
#
## ----CS_never, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_CS_never <- function(...) {
#
# # resimulate the data
# data <- make_data2()
# data$cohort_year[data$cohort_year==Inf] <- 0
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "never_treated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS_never <- map_dfr(1:nrep, run_CS_never)
#
# ES_plot_CS_never <- data_CS_never %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Never-treated units")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS_never
#
# #ggsave("es_plot_CS_never.png", ES_plot_CS_never, width = 10, height = 5, dpi = 150)
## ----CS_ny, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
#
# # function to run ES DID
# run_CS_ny <- function(...) {
#
# # resimulate the data
# data <- make_data2()
# data$cohort_year[data$cohort_year==Inf] <- 0
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "notyettreated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS_ny <- map_dfr(1:nrep, run_CS_ny)
#
# ES_plot_CS_ny <- data_CS_ny %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Not-yet-treated units")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS_ny
#
# #ggsave("es_plot_CS_ny.png", ES_plot_CS_ny, width = 10, height = 5, dpi = 150)
## ----DGP_viz3, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
#
# ## Generate data - treated cohorts consist of 250 obs each, with the treatment effect still = true_mu on average
# make_data3 <- function(nobs = 1000,
# nstates = 40) {
#
# # unit fixed effects (unobservd heterogeneity)
# unit <- tibble(
# unit = 1:nobs,
# # generate state
# state = sample(1:nstates, nobs, replace = TRUE),
# unit_fe = rnorm(nobs, state/5, 1),
# # generate instantaneous treatment effect
# #mu = rnorm(nobs, true_mu, 0.2)
# mu = true_mu
# )
#
# # year fixed effects (first part)
# year <- tibble(
# year = 1980:2010,
# year_fe = rnorm(length(year), 0, 1)
# )
#
# # Put the states into treatment groups
# treat_taus <- tibble(
# # sample the states randomly
# state = sample(1:nstates, nstates, replace = FALSE),
# # place the randomly sampled states into 1\{t \ge g \}G_g
# cohort_year = sort(rep(c(1986, 1992, 1998, 2004), 10))
# )
#
# # make main dataset
# # full interaction of unit X year
# expand_grid(unit = 1:nobs, year = 1980:2010) %>%
# left_join(., unit) %>%
# left_join(., year) %>%
# left_join(., treat_taus) %>%
# # make error term and get treatment indicators and treatment effects
# # Also get cohort specific trends (modify time FE)
# mutate(error = rnorm(nobs*31, 0, 1),
# treat = ifelse((year >= cohort_year)* (cohort_year != 2004), 1, 0),
# mu = ifelse(cohort_year==1992, 2, ifelse(cohort_year==1998, 1, 3)),
# tau = ifelse(treat == 1, mu, 0),
# year_fe = year_fe + 0.1*(year - cohort_year)
# ) %>%
# # calculate cumulative treatment effects
# group_by(unit) %>%
# mutate(tau_cum = cumsum(tau)) %>%
# ungroup() %>%
# # calculate the dep variable
# mutate(dep_var = (2010 - cohort_year) + unit_fe + year_fe + tau_cum + error) %>%
# # Relabel 2004 cohort as never-treated
# mutate(cohort_year = ifelse(cohort_year == 2004, Inf, cohort_year))
#
# }
# #----------------------------------------------------------------------------
# # make data
# data <- make_data3()
#
# # plot
# plot3 <- data %>%
# ggplot(aes(x = year, y = dep_var, group = unit)) +
# geom_line(alpha = 1/8, color = "grey") +
# geom_line(data = data %>%
# group_by(cohort_year, year) %>%
# summarize(dep_var = mean(dep_var)),
# aes(x = year, y = dep_var, group = factor(cohort_year),
# color = factor(cohort_year)),
# size = 2) +
# labs(x = "", y = "Value", color = "Treatment group ") +
# geom_vline(xintercept = 1986, color = '#E41A1C', size = 2) +
# geom_vline(xintercept = 1992, color = '#377EB8', size = 2) +
# geom_vline(xintercept = 1998, color = '#4DAF4A', size = 2) +
# #geom_vline(xintercept = 2004, color = '#984EA3', size = 2) +
# scale_color_brewer(palette = 'Set1') +
# theme(legend.position = 'bottom',
# #legend.title = element_blank(),
# axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# scale_color_manual(labels = c("1986", "1992", "1998", "Never-treated"),
# values = c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3")) +
# ggtitle("One draw of the DGP with heterogeneous treatment effect dynamics across cohorts \n and with a never-treated group")+
# theme(plot.title = element_text(hjust = 0.5, size=12))
#
# plot3
#
# #ggsave("plot_dgp_never_treated_het.png", plot3, width = 10, height = 5, dpi = 100)
## ----ES1_never_het, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# # variables we will use
# keepvars <- c("`rel_year_-5`", "`rel_year_-4`", "`rel_year_-3`", "`rel_year_-2`",
# "rel_year_0", "rel_year_1", "rel_year_2", "rel_year_3", "rel_year_4", "rel_year_5")
#
# run_ES_DiD_never_het <- function(...) {
#
# # resimulate the data
# data <- make_data3()
# # make dummy columns
# data <- data %>%
# # make dummies
# mutate(rel_year = year - cohort_year) %>%
# mutate(rel_year = ifelse(rel_year == -Inf, NA, rel_year))%>%
# dummy_cols(select_columns = "rel_year") %>%
# mutate(across(starts_with("rel_year_"), ~replace_na(., 0))) %>%
# # generate pre and post dummies
# mutate(Pre = ifelse((rel_year < -5) * (!is.na(rel_year)), 1, 0),
# Post = ifelse((rel_year > 5) * (!is.na(rel_year)), 1, 0)) %>%
# mutate(Pre = ifelse(is.na(Pre), 0, Pre),
# Post = ifelse(is.na(Post), 0, Post))
#
# # estimate the model
# mod <- lfe::felm(dep_var ~ Pre + `rel_year_-5` + `rel_year_-4` + `rel_year_-3` + `rel_year_-2` +
# `rel_year_0` + `rel_year_1` + `rel_year_2` + `rel_year_3` + `rel_year_4` +
# `rel_year_5` + Post | unit + year | 0 | state, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_classical_never_het <- map_dfr(1:nrep, run_ES_DiD_never_het)
#
# ES_plot_classical_never_het <- data_classical_never_het %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* 2, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("TWFE event-study regression with binned end-points")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_classical_never_het
#
# #ggsave("es_plot_classical_never_het.png", ES_plot_classical_never_het, width = 10, height = 5, dpi = 150)
## ----ES3_never_het, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_ES_DiD_sat_never_het <- function(...) {
#
# # resimulate the data
# data <- make_data3()
#
# # make dummy columns
# data <- data %>%
# # make relative year indicator
# mutate(rel_year = year - cohort_year)
#
# # get the minimum relative year - we need this to reindex
# min_year <- min(data$rel_year * (data$rel_year != -Inf), na.rm = T)
#
# # reindex the relative years
# data <- data %>%
# mutate(rel_year2 = rel_year) %>%
# mutate(rel_year = rel_year - min_year) %>%
# dummy_cols(select_columns = "rel_year") %>%
# select(-("rel_year_-Inf"))
#
#
# # make regression formula
# indics <- paste("rel_year", (1:max(data$rel_year))[-(-1 - min_year)], sep = "_", collapse = " + ")
# keepvars <- paste("rel_year", c(-5:-2, 0:5) - min_year, sep = "_")
# formula <- as.formula(paste("dep_var ~", indics, "| unit + year | 0 | state"))
#
# # run mod
# mod <- felm(formula, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_sat_never_het <- map_dfr(1:nrep, run_ES_DiD_sat_never_het)
#
# ES_plot_sat_never_het <- data_sat_never_het %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* 2, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("TWFE event-study regression with 'all' leads and lags")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_sat_never_het
#
# #ggsave("es_plot_sat_never_het.png", ES_plot_sat_never_het, width = 10, height = 5, dpi = 150)
#
## ----CS_never_het, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_CS_never_het <- function(...) {
#
# # resimulate the data
# data <- make_data3()
# data$cohort_year[data$cohort_year==Inf] <- 0
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "never_treated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS_never_het <- map_dfr(1:nrep, run_CS_never_het)
#
# ES_plot_CS_never_het <- data_CS_never_het %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* 2, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Never-treated units")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS_never_het
#
# #ggsave("es_plot_CS_never_het.png", ES_plot_CS_never_het, width = 10, height = 5)
## ----CS_ny_het, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
#
# # function to run ES DID
# run_CS_ny_het <- function(...) {
#
# # resimulate the data
# data <- make_data3()
# data$cohort_year[data$cohort_year==Inf] <- 0
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "notyettreated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS_ny_het <- map_dfr(1:nrep, run_CS_ny_het)
#
# ES_plot_CS_ny_het <- data_CS_ny_het %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* 2, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Not-yet-treated units")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS_ny_het
#
# #ggsave("es_plot_CS_ny_het.png", ES_plot_CS_ny_het, width = 10, height = 5, dpi = 150)
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## ----setup2, include = FALSE--------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
echo = TRUE,
eval = FALSE
)
## ----DGP_viz, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # Load libraries and set baseline parameters
# library(tidyverse)
# library(lfe)
# library(fastDummies)
# library(ggthemes)
# library(did)
# theme_set(theme_clean() + theme(plot.background = element_blank()))
# #----------------------------------------------------------------------------
# iseed = 20201221
# nrep <- 100
# true_mu <- 1
# set.seed(iseed)
#
# #----------------------------------------------------------------------------
# ## Generate data - treated cohorts consist of 250 obs each, with the treatment effect still = true_mu on average
# make_data <- function(nobs = 1000,
# nstates = 40) {
#
# # unit fixed effects (unobservd heterogeneity)
# unit <- tibble(
# unit = 1:nobs,
# # generate state
# state = sample(1:nstates, nobs, replace = TRUE),
# unit_fe = rnorm(nobs, state/5, 1),
# # generate instantaneous treatment effect
# #mu = rnorm(nobs, true_mu, 0.2)
# mu = true_mu
# )
#
# # year fixed effects (first part)
# year <- tibble(
# year = 1980:2010,
# year_fe = rnorm(length(year), 0, 1)
# )
#
# # Put the states into treatment groups
# treat_taus <- tibble(
# # sample the states randomly
# state = sample(1:nstates, nstates, replace = FALSE),
# # place the randomly sampled states into four treatment groups G_g
# cohort_year = sort(rep(c(1986, 1992, 1998, 2004), 10))
# )
#
# # make main dataset
# # full interaction of unit X year
# expand_grid(unit = 1:nobs, year = 1980:2010) %>%
# left_join(., unit) %>%
# left_join(., year) %>%
# left_join(., treat_taus) %>%
# # make error term and get treatment indicators and treatment effects
# # Also get cohort specific trends (modify time FE)
# mutate(error = rnorm(nobs*31, 0, 1),
# treat = ifelse(year >= cohort_year, 1, 0),
# tau = ifelse(treat == 1, mu, 0),
# year_fe = year_fe + 0.1*(year - cohort_year)
# ) %>%
# # calculate cumulative treatment effects
# group_by(unit) %>%
# mutate(tau_cum = cumsum(tau)) %>%
# ungroup() %>%
# # calculate the dep variable
# mutate(dep_var = (2010 - cohort_year) + unit_fe + year_fe + tau_cum + error)
#
# }
# #----------------------------------------------------------------------------
# # make data
# data <- make_data()
#
# # plot
# plot1 <- data %>%
# ggplot(aes(x = year, y = dep_var, group = unit)) +
# geom_line(alpha = 1/8, color = "grey") +
# geom_line(data = data %>%
# group_by(cohort_year, year) %>%
# summarize(dep_var = mean(dep_var)),
# aes(x = year, y = dep_var, group = factor(cohort_year),
# color = factor(cohort_year)),
# size = 2) +
# labs(x = "", y = "Value", color = "Treatment group ") +
# geom_vline(xintercept = 1986, color = '#E41A1C', size = 2) +
# geom_vline(xintercept = 1992, color = '#377EB8', size = 2) +
# geom_vline(xintercept = 1998, color = '#4DAF4A', size = 2) +
# geom_vline(xintercept = 2004, color = '#984EA3', size = 2) +
# scale_color_brewer(palette = 'Set1') +
# theme(legend.position = 'bottom',
# #legend.title = element_blank(),
# axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# ggtitle("One draw of the DGP with homogeneous effects across cohorts \n and with all groups being eventually treated")+
# theme(plot.title = element_text(hjust = 0.5, size=12))
#
# plot1
#
# #ggsave("plot_dgp1.png", plot1, width = 10, height = 5, dpi = 100)
## ----ES1, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# # variables we will use
# keepvars <- c("`rel_year_-5`", "`rel_year_-4`", "`rel_year_-3`", "`rel_year_-2`",
# "rel_year_0", "rel_year_1", "rel_year_2", "rel_year_3", "rel_year_4", "rel_year_5")
#
# run_ES_DiD <- function(...) {
#
# # resimulate the data
# data <- make_data()
#
# # make dummy columns
# data <- data %>%
# # make dummies
# mutate(rel_year = year - cohort_year) %>%
# dummy_cols(select_columns = "rel_year") %>%
# # generate pre and post dummies
# mutate(Pre = ifelse(rel_year < -5, 1, 0),
# Post = ifelse(rel_year > 5, 1, 0))
#
# # estimate the model
# mod <- lfe::felm(dep_var ~ Pre + `rel_year_-5` + `rel_year_-4` + `rel_year_-3` + `rel_year_-2` +
# `rel_year_0` + `rel_year_1` + `rel_year_2` + `rel_year_3` + `rel_year_4` +
# `rel_year_5` + Post | unit + year | 0 | state, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_classical <- map_dfr(1:nrep, run_ES_DiD)
#
# colors <- c("True Effect" = "red", "Estimated Effect" = "blue")
#
# ES_plot_classical <- data_classical %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# ggtitle("TWFE event-study regression with binned end-points")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_classical
#
# #ggsave("es_plot_classical.png", ES_plot_classical, width = 10, height = 5, dpi = 150)
## ----ES3, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_ES_DiD_sat <- function(...) {
#
# # resimulate the data
# data <- make_data()
#
# # make dummy columns
# data <- data %>%
# # make relative year indicator
# mutate(rel_year = year - cohort_year)
#
# # get the minimum relative year - we need this to reindex
# min_year <- min(data$rel_year)
#
# # reindex the relative years
# data <- data %>%
# mutate(rel_year = rel_year - min_year) %>%
# dummy_cols(select_columns = "rel_year")
#
# # make regression formula
# indics <- paste("rel_year", (1:max(data$rel_year))[-(-1 - min_year)], sep = "_", collapse = " + ")
# keepvars <- paste("rel_year", c(-5:-2, 0:5) - min_year, sep = "_")
# formula <- as.formula(paste("dep_var ~", indics, "| unit + year | 0 | state"))
#
# # run mod
# mod <- felm(formula, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_sat <- map_dfr(1:nrep, run_ES_DiD_sat)
#
# ES_plot_sat <- data_sat %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# ggtitle("TWFE event-study regression with 'all' leads and lags")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
#
# ES_plot_sat
#
# #ggsave("es_plot_sat.png", ES_plot_sat, width = 10, height = 5, dpi = 150)
#
## ----CS, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_CS <- function(...) {
#
# # resimulate the data
# data <- make_data()
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "notyettreated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS <- map_dfr(1:nrep, run_CS)
#
# ES_plot_CS <- data_CS %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Not-yet-treated")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS
#
# #ggsave("es_plot_CS.png", ES_plot_CS, width = 10, height = 5, dpi = 150)
#
## ----DGP_viz2, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
#
# ## Generate data - treated cohorts consist of 250 obs each, with the treatment effect still = true_mu on average
# make_data2 <- function(nobs = 1000,
# nstates = 40) {
#
# # unit fixed effects (unobservd heterogeneity)
# unit <- tibble(
# unit = 1:nobs,
# # generate state
# state = sample(1:nstates, nobs, replace = TRUE),
# unit_fe = rnorm(nobs, state/5, 1),
# # generate instantaneous treatment effect
# #mu = rnorm(nobs, true_mu, 0.2)
# mu = true_mu
# )
#
# # year fixed effects (first part)
# year <- tibble(
# year = 1980:2010,
# year_fe = rnorm(length(year), 0, 1)
# )
#
# # Put the states into treatment groups
# treat_taus <- tibble(
# # sample the states randomly
# state = sample(1:nstates, nstates, replace = FALSE),
# # place the randomly sampled states into 1\{t \ge g \}G_g
# cohort_year = sort(rep(c(1986, 1992, 1998, 2004), 10))
# )
#
# # make main dataset
# # full interaction of unit X year
# expand_grid(unit = 1:nobs, year = 1980:2010) %>%
# left_join(., unit) %>%
# left_join(., year) %>%
# left_join(., treat_taus) %>%
# # make error term and get treatment indicators and treatment effects
# # Also get cohort specific trends (modify time FE)
# mutate(error = rnorm(nobs*31, 0, 1),
# treat = ifelse((year >= cohort_year)* (cohort_year != 2004), 1, 0),
# tau = ifelse(treat == 1, mu, 0),
# year_fe = year_fe + 0.1*(year - cohort_year)
# ) %>%
# # calculate cumulative treatment effects
# group_by(unit) %>%
# mutate(tau_cum = cumsum(tau)) %>%
# ungroup() %>%
# # calculate the dep variable
# mutate(dep_var = (2010 - cohort_year) + unit_fe + year_fe + tau_cum + error) %>%
# # Relabel 2004 cohort as never-treated
# mutate(cohort_year = ifelse(cohort_year == 2004, Inf, cohort_year))
#
# }
# #----------------------------------------------------------------------------
# # make data
# data <- make_data2()
#
# # plot
# plot2 <- data %>%
# ggplot(aes(x = year, y = dep_var, group = unit)) +
# geom_line(alpha = 1/8, color = "grey") +
# geom_line(data = data %>%
# group_by(cohort_year, year) %>%
# summarize(dep_var = mean(dep_var)),
# aes(x = year, y = dep_var, group = factor(cohort_year),
# color = factor(cohort_year)),
# size = 2) +
# labs(x = "", y = "Value", color = "Treatment group ") +
# geom_vline(xintercept = 1986, color = '#E41A1C', size = 2) +
# geom_vline(xintercept = 1992, color = '#377EB8', size = 2) +
# geom_vline(xintercept = 1998, color = '#4DAF4A', size = 2) +
# #geom_vline(xintercept = 2004, color = '#984EA3', size = 2) +
# scale_color_brewer(palette = 'Set1') +
# theme(legend.position = 'bottom',
# #legend.title = element_blank(),
# axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# scale_color_manual(labels = c("1986", "1992", "1998", "Never-treated"),
# values = c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3"))+
# ggtitle("One draw of the DGP with homogeneous effects across cohorts \n and with a never-treated group")+
# theme(plot.title = element_text(hjust = 0.5, size=12))
#
# plot2
#
# #ggsave("plot_dgp_never_treated.png",plot2, width = 10, height = 5, dpi = 100)
## ----ES1_never, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# # variables we will use
# keepvars <- c("`rel_year_-5`", "`rel_year_-4`", "`rel_year_-3`", "`rel_year_-2`",
# "rel_year_0", "rel_year_1", "rel_year_2", "rel_year_3", "rel_year_4", "rel_year_5")
#
# run_ES_DiD_never <- function(...) {
#
# # resimulate the data
# data <- make_data2()
# # make dummy columns
# data <- data %>%
# # make dummies
# mutate(rel_year = year - cohort_year) %>%
# mutate(rel_year = ifelse(rel_year == -Inf, NA, rel_year))%>%
# dummy_cols(select_columns = "rel_year") %>%
# mutate(across(starts_with("rel_year_"), ~replace_na(., 0))) %>%
# # generate pre and post dummies
# mutate(Pre = ifelse((rel_year < -5) * (!is.na(rel_year)), 1, 0),
# Post = ifelse((rel_year > 5) * (!is.na(rel_year)), 1, 0)) %>%
# mutate(Pre = ifelse(is.na(Pre), 0, Pre),
# Post = ifelse(is.na(Post), 0, Post))
#
# # estimate the model
# mod <- lfe::felm(dep_var ~ Pre + `rel_year_-5` + `rel_year_-4` + `rel_year_-3` + `rel_year_-2` +
# `rel_year_0` + `rel_year_1` + `rel_year_2` + `rel_year_3` + `rel_year_4` +
# `rel_year_5` + Post | unit + year | 0 | state, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_classical_never <- map_dfr(1:nrep, run_ES_DiD_never)
#
# ES_plot_classical_never <- data_classical_never %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("TWFE event-study regression with binned end-points")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_classical_never
#
# #ggsave("es_plot_classical_never.png", ES_plot_classical_never, width = 10, height = 5, dpi = 150)
## ----ES3_never, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_ES_DiD_sat_never <- function(...) {
#
# # resimulate the data
# data <- make_data2()
#
# # make dummy columns
# data <- data %>%
# # make relative year indicator
# mutate(rel_year = year - cohort_year)
#
# # get the minimum relative year - we need this to reindex
# min_year <- min(data$rel_year * (data$rel_year != -Inf), na.rm = T)
#
# # reindex the relative years
# data <- data %>%
# mutate(rel_year2 = rel_year) %>%
# mutate(rel_year = rel_year - min_year) %>%
# dummy_cols(select_columns = "rel_year") %>%
# select(-("rel_year_-Inf"))
#
#
# # make regression formula
# indics <- paste("rel_year", (1:max(data$rel_year))[-(-1 - min_year)], sep = "_", collapse = " + ")
# keepvars <- paste("rel_year", c(-5:-2, 0:5) - min_year, sep = "_")
# formula <- as.formula(paste("dep_var ~", indics, "| unit + year | 0 | state"))
#
# # run mod
# mod <- felm(formula, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_sat_never <- map_dfr(1:nrep, run_ES_DiD_sat_never)
#
# ES_plot_sat_never <- data_sat_never %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("TWFE event-study regression with 'all' leads and lags")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_sat_never
#
# #ggsave("es_plot_sat_never.png", ES_plot_sat_never, width = 10, height = 5, dpi = 150)
#
## ----CS_never, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_CS_never <- function(...) {
#
# # resimulate the data
# data <- make_data2()
# data$cohort_year[data$cohort_year==Inf] <- 0
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "never_treated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS_never <- map_dfr(1:nrep, run_CS_never)
#
# ES_plot_CS_never <- data_CS_never %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Never-treated units")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS_never
#
# #ggsave("es_plot_CS_never.png", ES_plot_CS_never, width = 10, height = 5, dpi = 150)
## ----CS_ny, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
#
# # function to run ES DID
# run_CS_ny <- function(...) {
#
# # resimulate the data
# data <- make_data2()
# data$cohort_year[data$cohort_year==Inf] <- 0
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "notyettreated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS_ny <- map_dfr(1:nrep, run_CS_ny)
#
# ES_plot_CS_ny <- data_CS_ny %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* true_mu, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Not-yet-treated units")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS_ny
#
# #ggsave("es_plot_CS_ny.png", ES_plot_CS_ny, width = 10, height = 5, dpi = 150)
## ----DGP_viz3, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
#
# ## Generate data - treated cohorts consist of 250 obs each, with the treatment effect still = true_mu on average
# make_data3 <- function(nobs = 1000,
# nstates = 40) {
#
# # unit fixed effects (unobservd heterogeneity)
# unit <- tibble(
# unit = 1:nobs,
# # generate state
# state = sample(1:nstates, nobs, replace = TRUE),
# unit_fe = rnorm(nobs, state/5, 1),
# # generate instantaneous treatment effect
# #mu = rnorm(nobs, true_mu, 0.2)
# mu = true_mu
# )
#
# # year fixed effects (first part)
# year <- tibble(
# year = 1980:2010,
# year_fe = rnorm(length(year), 0, 1)
# )
#
# # Put the states into treatment groups
# treat_taus <- tibble(
# # sample the states randomly
# state = sample(1:nstates, nstates, replace = FALSE),
# # place the randomly sampled states into 1\{t \ge g \}G_g
# cohort_year = sort(rep(c(1986, 1992, 1998, 2004), 10))
# )
#
# # make main dataset
# # full interaction of unit X year
# expand_grid(unit = 1:nobs, year = 1980:2010) %>%
# left_join(., unit) %>%
# left_join(., year) %>%
# left_join(., treat_taus) %>%
# # make error term and get treatment indicators and treatment effects
# # Also get cohort specific trends (modify time FE)
# mutate(error = rnorm(nobs*31, 0, 1),
# treat = ifelse((year >= cohort_year)* (cohort_year != 2004), 1, 0),
# mu = ifelse(cohort_year==1992, 2, ifelse(cohort_year==1998, 1, 3)),
# tau = ifelse(treat == 1, mu, 0),
# year_fe = year_fe + 0.1*(year - cohort_year)
# ) %>%
# # calculate cumulative treatment effects
# group_by(unit) %>%
# mutate(tau_cum = cumsum(tau)) %>%
# ungroup() %>%
# # calculate the dep variable
# mutate(dep_var = (2010 - cohort_year) + unit_fe + year_fe + tau_cum + error) %>%
# # Relabel 2004 cohort as never-treated
# mutate(cohort_year = ifelse(cohort_year == 2004, Inf, cohort_year))
#
# }
# #----------------------------------------------------------------------------
# # make data
# data <- make_data3()
#
# # plot
# plot3 <- data %>%
# ggplot(aes(x = year, y = dep_var, group = unit)) +
# geom_line(alpha = 1/8, color = "grey") +
# geom_line(data = data %>%
# group_by(cohort_year, year) %>%
# summarize(dep_var = mean(dep_var)),
# aes(x = year, y = dep_var, group = factor(cohort_year),
# color = factor(cohort_year)),
# size = 2) +
# labs(x = "", y = "Value", color = "Treatment group ") +
# geom_vline(xintercept = 1986, color = '#E41A1C', size = 2) +
# geom_vline(xintercept = 1992, color = '#377EB8', size = 2) +
# geom_vline(xintercept = 1998, color = '#4DAF4A', size = 2) +
# #geom_vline(xintercept = 2004, color = '#984EA3', size = 2) +
# scale_color_brewer(palette = 'Set1') +
# theme(legend.position = 'bottom',
# #legend.title = element_blank(),
# axis.title = element_text(size = 14),
# axis.text = element_text(size = 12)) +
# scale_color_manual(labels = c("1986", "1992", "1998", "Never-treated"),
# values = c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3")) +
# ggtitle("One draw of the DGP with heterogeneous treatment effect dynamics across cohorts \n and with a never-treated group")+
# theme(plot.title = element_text(hjust = 0.5, size=12))
#
# plot3
#
# #ggsave("plot_dgp_never_treated_het.png", plot3, width = 10, height = 5, dpi = 100)
## ----ES1_never_het, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# # variables we will use
# keepvars <- c("`rel_year_-5`", "`rel_year_-4`", "`rel_year_-3`", "`rel_year_-2`",
# "rel_year_0", "rel_year_1", "rel_year_2", "rel_year_3", "rel_year_4", "rel_year_5")
#
# run_ES_DiD_never_het <- function(...) {
#
# # resimulate the data
# data <- make_data3()
# # make dummy columns
# data <- data %>%
# # make dummies
# mutate(rel_year = year - cohort_year) %>%
# mutate(rel_year = ifelse(rel_year == -Inf, NA, rel_year))%>%
# dummy_cols(select_columns = "rel_year") %>%
# mutate(across(starts_with("rel_year_"), ~replace_na(., 0))) %>%
# # generate pre and post dummies
# mutate(Pre = ifelse((rel_year < -5) * (!is.na(rel_year)), 1, 0),
# Post = ifelse((rel_year > 5) * (!is.na(rel_year)), 1, 0)) %>%
# mutate(Pre = ifelse(is.na(Pre), 0, Pre),
# Post = ifelse(is.na(Post), 0, Post))
#
# # estimate the model
# mod <- lfe::felm(dep_var ~ Pre + `rel_year_-5` + `rel_year_-4` + `rel_year_-3` + `rel_year_-2` +
# `rel_year_0` + `rel_year_1` + `rel_year_2` + `rel_year_3` + `rel_year_4` +
# `rel_year_5` + Post | unit + year | 0 | state, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_classical_never_het <- map_dfr(1:nrep, run_ES_DiD_never_het)
#
# ES_plot_classical_never_het <- data_classical_never_het %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* 2, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("TWFE event-study regression with binned end-points")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_classical_never_het
#
# #ggsave("es_plot_classical_never_het.png", ES_plot_classical_never_het, width = 10, height = 5, dpi = 150)
## ----ES3_never_het, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_ES_DiD_sat_never_het <- function(...) {
#
# # resimulate the data
# data <- make_data3()
#
# # make dummy columns
# data <- data %>%
# # make relative year indicator
# mutate(rel_year = year - cohort_year)
#
# # get the minimum relative year - we need this to reindex
# min_year <- min(data$rel_year * (data$rel_year != -Inf), na.rm = T)
#
# # reindex the relative years
# data <- data %>%
# mutate(rel_year2 = rel_year) %>%
# mutate(rel_year = rel_year - min_year) %>%
# dummy_cols(select_columns = "rel_year") %>%
# select(-("rel_year_-Inf"))
#
#
# # make regression formula
# indics <- paste("rel_year", (1:max(data$rel_year))[-(-1 - min_year)], sep = "_", collapse = " + ")
# keepvars <- paste("rel_year", c(-5:-2, 0:5) - min_year, sep = "_")
# formula <- as.formula(paste("dep_var ~", indics, "| unit + year | 0 | state"))
#
# # run mod
# mod <- felm(formula, data = data, exactDOF = TRUE)
#
# # grab the obs we need
# # grab the obs we need
# mod2 <- tibble(
# estimate = mod$coefficients,
# term1 = rownames(mod$coefficients)
# )
#
# es <-
# mod2 %>%
# filter(term1 %in% keepvars) %>%
# mutate(t = c(-5:-2, 0:5)) %>%
# select(t, estimate)
# es
# }
#
# data_sat_never_het <- map_dfr(1:nrep, run_ES_DiD_sat_never_het)
#
# ES_plot_sat_never_het <- data_sat_never_het %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# bind_rows(tibble(t = -1, avg = 0, sd = 0, lower.ci = 0, upper.ci = 0)) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* 2, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("TWFE event-study regression with 'all' leads and lags")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_sat_never_het
#
# #ggsave("es_plot_sat_never_het.png", ES_plot_sat_never_het, width = 10, height = 5, dpi = 150)
#
## ----CS_never_het, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
# # function to run ES DID
# run_CS_never_het <- function(...) {
#
# # resimulate the data
# data <- make_data3()
# data$cohort_year[data$cohort_year==Inf] <- 0
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "never_treated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS_never_het <- map_dfr(1:nrep, run_CS_never_het)
#
# ES_plot_CS_never_het <- data_CS_never_het %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* 2, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Never-treated units")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS_never_het
#
# #ggsave("es_plot_CS_never_het.png", ES_plot_CS_never_het, width = 10, height = 5)
## ----CS_ny_het, message = FALSE, warning = FALSE, cache = TRUE, cache.lazy = TRUE, fig.width=8,fig.height=5, fig.align='center', out.width="90%", dpi = 200----
#
# # function to run ES DID
# run_CS_ny_het <- function(...) {
#
# # resimulate the data
# data <- make_data3()
# data$cohort_year[data$cohort_year==Inf] <- 0
#
# mod <- did::att_gt(yname = "dep_var",
# tname = "year",
# idname = "unit",
# gname = "cohort_year",
# control_group= "notyettreated",
# bstrap = FALSE,
# data = data,
# print_details = FALSE)
# event_std <- did::aggte(mod, type = "dynamic")
#
# att.egt <- event_std$att.egt
# names(att.egt) <- event_std$egt
#
# # grab the obs we need
# broom::tidy(att.egt) %>%
# filter(names %in% -5:5) %>%
# mutate(t = -5:5, estimate = x) %>%
# select(t, estimate)
# }
#
# data_CS_ny_het <- map_dfr(1:nrep, run_CS_ny_het)
#
# ES_plot_CS_ny_het <- data_CS_ny_het %>%
# group_by(t) %>%
# summarize(avg = mean(estimate),
# sd = sd(estimate),
# lower.ci = avg - 1.96*sd,
# upper.ci = avg + 1.96*sd) %>%
# mutate(true_tau = ifelse(t >= 0, (t + 1)* 2, 0)) %>%
# ggplot(aes(x = t, y = avg)) +
# #geom_linerange(aes(ymin = lower.ci, ymax = upper.ci), color = 'darkgrey', size = 2) +
# geom_ribbon(aes(ymin = lower.ci, ymax = upper.ci), color = "lightgrey", alpha = 0.2) +
# geom_point(color = 'blue', size = 3) +
# geom_line(aes(color = 'Estimated Effect'), size = 1) +
# geom_line(aes(x = t, y = true_tau, color = 'True Effect'), linetype = "dashed", size = 2) +
# geom_hline(yintercept = 0, linetype = "dashed") +
# scale_x_continuous(breaks = -5:5) +
# labs(x = "Relative Time", y = "Estimate") +
# theme(axis.title = element_text(size = 14),
# axis.text = element_text(size = 12))+
# ggtitle("Event-study-parameters estimated using Callaway and Sant'Anna (2021)\nComparison group: Not-yet-treated units")+
# scale_color_manual(values = colors) +
# theme(plot.title = element_text(hjust = 0.5, size=12),
# legend.position = "bottom",
# legend.title = element_blank())
#
# ES_plot_CS_ny_het
#
# #ggsave("es_plot_CS_ny_het.png", ES_plot_CS_ny_het, width = 10, height = 5, dpi = 150)
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