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rdd_nopara.R
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rdd_nopara.R
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library(rddtools)
library(rdd)
library(rdrobust)
library(plm)
library(stargazer)
library(xtable)
library(plyr)
library(dplyr)
merge_rasters_dataframes$dist_p_km <- (merge_rasters_dataframes$dist_p) / 1000
#Table 1 - Descriptive statistics by group
vars <- c("altura_mean_30arc", "roughness", "slope", "colds00ag", "dist_coast", "dist_capital", "dist_colonial")
stats_table <- merge_rasters_dataframes %>%
group_by(treatment) %>%
summarize()
#Table 2 - RD Sharp for 2013
attach(merge_rasters_dataframes)
rd2013 <- rdrobust(x = dist_p_km,
y = log(0.01 + dm2013),
vce = "hc1",
all =T)
rd2013_controles <- rdrobust(x = dist_p_km,
y = log(0.01 + dm2013),
covs = cbind(altura_mean_30arc, roughness, slope, factor(sq1), dist_coast, dist_colonial,
dist_capital),
vce = "hc1",
all =T)
rd2013_controles_fx <- rdrobust(x = dist_p_km,
y = log(0.01 + dm2013),
covs = cbind(altura_mean_30arc, roughness, slope, factor(sq1), dist_coast, dist_colonial,
dist_capital, factor(municode), factor(dptocode)),
cluster = ID + municode,
vce = "hc1",
all =T)
rd2013_controles_fx_nn <- rdrobust(x = dist_p_km,
y = log(0.01 + dm2013),
covs = cbind(altura_mean_30arc, roughness, slope, factor(sq1), dist_coast, dist_colonial,
dist_capital, factor(municode), factor(dptocode)),
vce = "nn", nnmatch = 8,
all =T)
detach(merge_rasters_dataframes)
stargazer(t(rbind(t(rd2013$tabl3.str[3, ]), t(rd2013_controles$tabl3.str[3, ]), t(rd2013_controles_fx$tabl3.str[3, ]),
t(rd2013_controles_fx_nn$tabl3.str[3, ]))))
#Robustness table - covariates RD
convariates <- c("")
rd2013_rdd <- rdd::RDestimate(log(0.01 + slope) ~ dist_p_km ,
data = merge_rasters_dataframes,
cluster = merge_rasters_dataframes$ID,
cutpoint = 0,
frame = T
)
#Table 4 - Panel data
#Graph 1 - RD estimates
attach(merge_rasters_dataframes)
g_rd1 <- rdplot(x = dist_p_km,
y = log(0.01 + dm2013),
binselect = "esmv",
x.lim = c(-10, 10),
y.lim = c(-5, -3),
x.label = "Distancia normalizada a la frontera (km.)",
y.label = "Logaritmo densidad de luz",
title = "")
detach(merge_rasters_dataframes)
#Table 3 - RD estimates for cavariates
attach(merge_rasters_dataframes)
rd2013_roughness <- rdrobust(x = dist_p_km,
y = log(0.01 + roughness),
vce = "hc1",
cluster = municode + ID + dptocode,
all =T)
detach(merge_rasters_dataframes)
#Data frames by distance
merge_rasters_bw <- list()
for(i in c(100, 200, 300, 400, 500, 1000, 2500, 2000)){
merge_rasters_bw[[str_c(i)]] <- filter(merge_rasters_dataframes, dist_p < i, dist_p > -i)
}
#Data frames by department
merge_rasters_dataframes_depto <- split(merge_rasters_dataframes, merge_rasters_dataframes$dptocode)
#RDD Tools
discontinuity_data <- rddtools::rdd_data(x = dist_p_km,
y = log(0.01 + dm2013),
data = merge_rasters_dataframes,
cutpoint = 0)
reg_para <- RDDreg_lm(discontinuity_data, order = 1, covariates = merge_rasters_dataframes[, 1:10])
reg_nonpara <- rdd_reg_np(discontinuity_data)
reg_para <- RDDreg_lm(discontinuity_data, order = 1)
bw_ik <- RDDbw_IK(discontinuity_data)
reg_nonpara <- RDDreg_np(RDDobject = discontinuity_data, bw = bw_ik)
plotSensi(reg_nonpara, from = 1, to = 50, by = 5)
plotPlacebo(reg_nonpara)
#rdrobust pacakge
#Variables <- cut-off (dist_p) and outcome (all the dm)
dist <- merge_rasters_dataframes$dist_p_km
light <- cbind(merge_rasters_dataframes[14:35])
#Estimation for all years
rd_nonpara <- list()
for(i in c(1:22)){
rd_nonpara[[str_c(i)]] <- rdrobust(x = dist, y = light[, i])
}
#Extract LATE's from list and create a table
rd_nonpara_table <- list() #Table of LATE and p-values
for(i in 1:length(rd_nonpara)){
rd_nonpara_table[[i]] <- rd_nonpara[[i]]$tabl3.str[1, ]
}
rd_nonpara_table <- ldply(rd_nonpara_table) #Reshape and create a table of LATE's
rd_nonpara_table$year <- c(1992:2013)
for(i in 1:length(rd_nonpara_table)){
rd_nonpara_table[, i] <- as.numeric(rd_nonpara_table[, i])
}
#Graph LATE for all years with IC's
theme_set(theme_gray(base_size = 13))
g1 <- ggplot(rd_nonpara_table, aes(year)) +
geom_line(aes(y = Coef), colour = "blue") +
geom_ribbon(aes(ymin = rd_nonpara_table$`CI Lower`, ymax = rd_nonpara_table$`CI Upper`), alpha = 0.2)
g1 <- g1 + scale_x_continuous(breaks=c(1992:2013))
g1 <- g1 + theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5, size = 10),
axis.text.y = element_text(size = 10))
g1 <- g1 + labs(x="Año", y=expression(paste("Diferencia (LATE)")), title="Discontinuidad por año - Estimación no paramétrica \n(1992- 2013)")
g1 <- g1 + theme(plot.title = element_text(size=20, face="bold",
margin = margin(10, 10, 10, 10)))
g1 <- g1 + geom_vline(xintercept=1997, linetype = 2)
png("LATE_local.png", width = 13, height = 9, units = 'in', res = 800)
g1
dev.off()
#rdlocrand package download (from Cattaneo's web-page)
url <- "http://www-personal.umich.edu/~cattaneo/software/rdlocrand/R/"
links <- getHTMLLinks(url)
filenames <- links[str_detect(links, ".R")]
filenames_list <- as.list(filenames)
l_ply(filenames_list, download,
baseurl = "http://www-personal.umich.edu/~cattaneo/software/rdlocrand/R/",
folder = "rdlocrand"
)
#rddensity package dowload (from Cattaneo's webpage)
url <- "http://www-personal.umich.edu/~cattaneo/software/rddensity/R/"
links <- getHTMLLinks(url)
filenames <- links[str_detect(links, ".R")]
filenames_list <- as.list(filenames)
l_ply(filenames_list, download,
baseurl = "http://www-personal.umich.edu/~cattaneo/software/rddensity/R/",
folder = "rddensity"
)
#Loading source commands of the package
setwd("rddensity")
source("rdbwdensity.R")
source("rddensity_fun.R")
source("rddensity.R")
#Loading source commands of the package
setwd("rdlocrand")
source("rdwinselect.R")
source("rdrandinf.R")
source("rdsensitivity.R")
source("rdrbounds.R")
#Define a window of treatment
covariates <- cbind(merge_rasters_dataframes$slope, merge_rasters_dataframes$roughness)
window <- rdwinselect(dist, covariates)
window <- rdrandinf(light[, 2], dist ,statistic = "all",
covariates = covariates, wmin = 10, wstep = 1,
rdwreps=10000, p = 2)
rd_nonpara_locrand <- list()
for(i in c(1:22)){
rd_nonpara_locrand[[str_c(i)]] <- rdrobust(x = dist, y = light[, i], h = 23, matches = 8)
}
rd_nonpara_locrand_table <- list() #Table of LATE and p-values
for(i in 1:length(rd_nonpara_locrand)){
rd_nonpara_locrand_table[[i]] <- rd_nonpara_locrand[[i]]$tabl3.str[1, ]
}
rd_nonpara_locrand_table <- ldply(rd_nonpara_locrand_table) #Reshape and create a table of LATE's
rownames(rd_nonpara_locrand_table) <- c(1992:2013)
rd_nonpara_locrand_table$year <- c(1992:2013)
for(i in 1:length(rd_nonpara_locrand_table)){
rd_nonpara_locrand_table[, i] <- as.numeric(rd_nonpara_locrand_table[, i])
}
stargazer(rd_nonpara_locrand_table[,c(1:6)], summary = F,
title = "Estimación no paramétrica para todos los años con supuestos de aletoriedad",
coef = rd_nonpara_locrand_table$`Coef `, se = rd_nonpara_locrand_table$`Std. Err.`,
p = rd_nonpara_locrand_table$`P>|z|`)
#Graph LATE for all years with IC's for random selected windows
theme_set(theme_gray(base_size = 13))
g2 <- ggplot(rd_nonpara_locrand_table, aes(year)) +
geom_line(aes(y = Coef), colour = "blue") +
geom_ribbon(aes(ymin = rd_nonpara_locrand_table$`CI Lower` , ymax = rd_nonpara_locrand_table$`CI Upper`), alpha = 0.2)
g2 <- g2 + scale_x_continuous(breaks=c(1992:2013))
g2 <- g2 + theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5, size = 10, face = "bold"),
axis.text.y = element_text(size = 10, face = "bold"))
g2 <- g2 + labs(x="Año", y=expression(paste("Diferencia (LATE)")), title="Discontinuidad por año - Estimación no paramétrica\n(1992- 2013)")
g2 <- g2 + theme(plot.title = element_text(size=20, face="bold", margin = margin(10, 10, 10, 10)),
axis.title = element_text(face="bold"))
g2 <- g2 + geom_vline(xintercept=1997, linetype = 2)
png("LATE_locrand.png", width = 13, height = 7, units = 'in', res = 800)
g2
dev.off()
g3 <- g2 + annotate("text", x = 1998, y = 0.1, label = "-0.5426") #Add label for 1998 LATE
png("LATE_locrand_98.png", width = 13, height = 7, units = 'in', res = 800)
g3
dev.off()
#RD graph approach
attach(merge_rasters_dataframes)
png("rd_1997.png", width = 13, height = 9, units = 'in', res = 800)
rdplot_1997 <- rdplot(y = dm1997, x = dist_p, c = 0, p = 2, binselect = "esmv",
x.label = "Distancia", y.label = "Actividad económica", y.lim = c(0, 2),
lowerend = -6000, upperend = 6000, col.lines = c(size = 10, color = "blue"),
title = "Discontinuidad en la actividad económica - 1997")
dev.off()
detach(merge_rasters_dataframes)
attach(merge_rasters_dataframes)
png("rd_2013.png", width = 13, height = 9, units = 'in', res = 800)
rdplot_2013 <- rdplot(y = dm2013, x = dist_p, c = 0, p = 2, binselect = "esmv",
x.label = "Distancia", y.label = "Actividad económica", y.lim = c(0, 2),
lowerend = -6000, upperend = 6000, col.lines = c(size = 10),
title = "Discontinuidad en la actividad económica - 2013")
dev.off()
detach(merge_rasters_dataframes)
#rdd package
merge_rasters_dataframes$dist_p_km <- merge_rasters_dataframes$dist_p / 1000
tratamientos <- names(merge_rasters_dataframes)[42:58]
luces <- names(merge_rasters_dataframes)[12:35]
controles <- c("altura_mean_30arc + roughness + slope + colds00ag + dist_coast + sq1 + sq2 + sq3 + sq4 + sq5 + sq6 + sq7")
rd_nonpara_ik <- list()
formulas_sharp <- paste(paste(paste("log(0.01 + ", luces, ")", sep = ""), "dist_p_km", sep = " ~ "), controles, sep = " | ")
formulas_fuzzy <- paste(paste(paste(paste("log(0.01 + ", luces, ")", sep = ""), "dist_p_km", sep = " ~ "), tratamientos, sep = " + "), controles, sep = " | ")
rd_nonpara_ik <- lapply(formulas_sharp, function(x) {
RDestimate(as.formula(x),
data = merge_rasters_dataframes_colonial,
cluster = merge_rasters_dataframes_colonial$municode,
cutpoint = 0,
model = TRUE)
})
rd_nonpara_ik_fuzzy <- lapply(formulas_fuzzy, function(x) {
RDestimate(as.formula(x),
data = merge_rasters_dataframes_colonial,
cluster = merge_rasters_dataframes_colonial$municode,
cutpoint = 0,
model = TRUE)
})
#Extract estimates and CI's.
rd_nonpara_ik_table <- lapply(rd_nonpara_ik, "[", c("est","p", "bw")) %>%
lapply(ldply) %>%
ldply() %>%
mutate(year = c(rep(1997:2013, each = 3))) %>%
setcolorder(c("year", "LATE", "Half-BW", "Double-BW", ".id")) %>%
mutate( year = as.factor(year)) %>%
select_(-.id)
stargazer(rd_nonpara_ik_table, summary = F)
rd_nonpara_ik_table <- lapply(rd_nonpara_ik, "[", c("est","p", "bw")) %>%
lapply(ldply) %>%
ldply() %>%
mutate(year = c(rep(1997:2013, each = 3))) %>%
setcolorder(c("year", "LATE", "Half-BW", "Double-BW", ".id")) %>%
mutate( year = as.factor(year)) %>%
reshape(direction = "wide", idvar = "year", timevar = ".id")
rd_nonpara_ik_table_ci <- lapply(rd_nonpara_ik, "[", c("ci")) %>%
lapply(ldply) %>%
lapply("[", 2, ) %>%
ldply() %>%
mutate(year = c(rep(1997:2013, each = 1))) %>%
reshape(direction = "wide", idvar = "year", timevar = ".id")
names(rd_nonpara_ik_table_ci)[3] <- "superior"
names(rd_nonpara_ik_table_ci)[2] <- "inferior"
#Merge LATE's data
rd_nonpara_ik_table <- cbind(rd_nonpara_ik_table, rd_nonpara_ik_table_ci)
#Graph ggplot estimates LATE's
library(ggplot2)
g2 <- ggplot(rd_nonpara_ik_table, aes(x = as.numeric(as.character(year)), y = LATE.est)) +
geom_ribbon(aes(ymin = rd_nonpara_ik_table$inferior , ymax = rd_nonpara_ik_table$superior), alpha = 0.2)
g2 <- g2 + geom_line() + theme_grey()
g2 <- g2 + scale_x_continuous(breaks=c(1992:2013)) + coord_fixed(ratio = 11)
g2 <- g2 + labs(x="Año", y=expression(paste("Diferencia (LATE)")))
g2 <- g2 + theme(axis.title.y = element_text(angle = 90, size = 12),
axis.title.x = element_text(angle = 0, size = 12))
g2 <- g2 + theme(axis.text.x = element_text(size = 10), axis.text.y = element_text(size = 10))
g2
png("~/Dropbox/BANREP/Pacifico/Primer_DTSER/Entregas/Imgs/DLATE_locrand.png", width = 13, height = 5, units = 'in', res = 800)
g2
dev.off()
#Graph discontinuities - 2013
par <- opar()
par(mfrow = c(3, 3))
plot(rd_nonpara_ik[[1]], 200 ,range = c(-5, 5), las = 1)
plot(rd_nonpara_ik[[2]], 200 ,range = c(-5, 5), las = 1)
plot(rd_nonpara_ik[[3]], 200 ,range = c(-5, 5), las = 1)
plot(rd_nonpara_ik[[4]], 200 ,range = c(-5, 5), las = 1)
plot(rd_nonpara_ik[[5]], 200 ,range = c(-5, 5), las = 1)
plot(rd_nonpara_ik[[6]], 200 ,range = c(-5, 5), las = 1)
plot(rd_nonpara_ik[[7]], 200 ,range = c(-5, 5), las = 1)
plot(rd_nonpara_ik[[8]], 200 ,range = c(-5, 5), las = 1)
plot(rd_nonpara_ik[[9]], 200 ,range = c(-5, 5), las = 1)
#RD for pixels near cities
formulas_fx <- paste(paste(paste("log(0.01 + " ,luces, ")" , sep = ""), "dist_p_km", sep = " ~ "), controles, sep = " | ")
merge_rasters_dataframes_cities <- subset(merge_rasters_dataframes, dist_capital < summary(dist_capital)[3])
rd_nonpara_ik_cities <- lapply(formulas_fx, function(x) {
RDestimate(as.formula(x),
data = merge_rasters_dataframes,
cutpoint = 0,
cluster = merge_rasters_dataframes_cities$municode,
verbose = T)
})
#RD for non treatment years
merge_rasters_dataframes$dist_p_km <- merge_rasters_dataframes$dist_p / 1000
tratamientos <- names(merge_rasters_dataframes)[42:59]
luces_no_tratamiento <- names(merge_rasters_dataframes)[14:18]
controles <- c("altura_mean_30arc + roughness + slope + colds00ag + dist_coast + dist_capital + dist_colonial")
rd_nonpara_ik <- list()
formulas_fx_nt <- paste(paste(paste("log(0.01 + " ,luces_no_tratamiento, ")" , sep = ""), "dist_p_km", sep = " ~ "), controles, sep = " | ")
rd_nonpara_ik_nt <- lapply(formulas_fx_nt, function(x) {
RDestimate(as.formula(x),
data = merge_rasters_dataframes,
cluster = merge_rasters_dataframes$municode,
cutpoint = 0,
model = TRUE)
})
#Extract estimates and CI's.
rd_nonpara_ik_nt_table <- lapply(rd_nonpara_ik_nt, "[", c("est","p", "bw")) %>%
lapply(ldply) %>%
ldply() %>%
mutate(year = c(rep(1992:1996, each = 3))) %>%
setcolorder(c("year", "LATE", "Half-BW", "Double-BW", ".id")) %>%
mutate( year = as.factor(year)) %>%
dplyr::select(-.id)
stargazer(rd_nonpara_ik_nt_table, summary = F)
rd_nonpara_ik_nt_table <- lapply(rd_nonpara_ik_nt, "[", c("est","p", "bw")) %>%
lapply(ldply) %>%
ldply() %>%
mutate(year = c(rep(1992:1996, each = 3))) %>%
setcolorder(c("year", "LATE", "Half-BW", "Double-BW", ".id")) %>%
mutate( year = as.factor(year)) %>%
reshape(direction = "wide", idvar = "year", timevar = ".id")
rd_nonpara_ik_nt_table_ci <- lapply(rd_nonpara_ik_nt, "[", c("ci")) %>%
lapply(ldply) %>%
lapply("[", 2, ) %>%
ldply() %>%
mutate(year = c(rep(1992:1996, each = 1))) %>%
reshape(direction = "wide", idvar = "year", timevar = ".id")
names(rd_nonpara_ik_nt_table_ci)[3] <- "superior"
names(rd_nonpara_ik_nt_table_ci)[2] <- "inferior"
#Merge LATE's data
rd_nonpara_ik_nt_table <- cbind(rd_nonpara_ik_nt_table, rd_nonpara_ik_nt_table_ci)
#Graph ggplot estimates LATE's
library(ggplot2)
g4 <- ggplot(rd_nonpara_ik_nt_table, aes(x = as.numeric(as.character(year)), y = LATE.est)) +
geom_ribbon(aes(ymin = rd_nonpara_ik_nt_table$inferior , ymax = rd_nonpara_ik_nt_table$superior), alpha = 0.2)
g4 <- g4 + geom_line() + theme_grey()
g4 <- g4 + scale_x_continuous(breaks=c(1992:1996)) + coord_fixed(ratio = 11)
g4 <- g4 + labs(x="Año", y=expression(paste("Diferencia (LATE)")))
g4 <- g4 + theme(axis.title.y = element_text(angle = 90, size = 12),
axis.title.x = element_text(angle = 0, size = 12))
g4 <- g4 + theme(axis.text.x = element_text(size = 10), axis.text.y = element_text(size = 10))
g4
#Placebo with random cutpoints (panel)
discontinuity_data <- RDDdata(x = dist_p_km,
y = log(0.01 + dm),
data = merge_rasters_dataframes_long,
cutpoint = 0)
reg_nonpara <- RDDreg_np(discontinuity_data)
placebo <- plotPlacebo(reg_nonpara, from = 0.1, to = 1, by = 0.1, same_bw = T, output = "ggplot")
placebo <- mutate(placebo, significativo = as.numeric(p_value > 0.05))
g3 <- ggplot(placebo, aes(x = cutpoint, y = LATE)) +
geom_ribbon(aes(ymin = placebo$CI_low, ymax = placebo$CI_high), alpha = 0.2)
g3 <- g3 + geom_line(aes(colour = placebo$position)) + theme_grey()
g3 <- g3 + scale_x_continuous(breaks = seq(-80, 40, by = 10))
g3 <- g3 + coord_fixed(ratio = 20) + geom_hline(yintercept = 0)
g3 <- g3 + labs(x="Punto de discontinuidad", y=expression(paste("Diferencia (LATE)")))
g3 <- g3 + theme(axis.title.y = element_text(angle = 90, size = 12),
axis.title.x = element_text(angle = 0, size = 12), legend.position = "none")
g3 <- g3 + theme(axis.text.x = element_text(size = 10), axis.text.y = element_text(size = 10))
g3
#Placebo with random cutpoints (2013)
discontinuity_data <- RDDdata(x = dist_p_km,
y = log(0.01 + dm2013),
data = merge_rasters_dataframes,
cutpoint = 0)
reg_nonpara_2013 <- RDDreg_np(discontinuity_data)
placebo_2013 <- plotPlacebo(reg_nonpara_2013, from = 0.1, to = 1, by = 0.1, same_bw = T, output = "data")
placebo_2013 <- mutate(placebo_2013, significativo = as.numeric(p_value > 0.05))
g4 <- ggplot(placebo_2013, aes(x = cutpoint, y = LATE)) +
geom_ribbon(aes(ymin = placebo_2013$CI_low, ymax = placebo_2013$CI_high), alpha = 0.2)
g4 <- g4 + geom_line(aes(colour = placebo_2013$position)) + theme_grey()
g4 <- g4 + scale_x_continuous(breaks = seq(-80, 40, by = 10))
g4 <- g4 + coord_fixed(ratio = 20) + geom_hline(yintercept = 0)
g4 <- g4 + labs(x="Punto de discontinuidad", y=expression(paste("Diferencia (LATE)")))
g4 <- g4 + theme(axis.title.y = element_text(angle = 90, size = 12),
axis.title.x = element_text(angle = 0, size = 12), legend.position = "none")
g4 <- g4 + theme(axis.text.x = element_text(size = 10), axis.text.y = element_text(size = 10))
g4
merge_rasters_dataframes$dist_p_km = merge_rasters_dataframes$dist_p / 1000
prueba <- RDestimate(log(0.01 + dm2013) ~ dist_p_km,
data = merge_rasters_dataframes,
cluster = merge_rasters_dataframes$municode,
cutpoint = 0,
model = TRUE)
attach(merge_rasters_dataframes)
prueba_robust <- rdrobust( y = log(0.01 + dm2013),
x = dist_p_km,
cluster = municode)
rdbwselect(y = log(0.01 + dm2013),
x = dist_p_km,
all = T)
rdplot(y = log(0.01 + dm2013), x = dist_p_km, c = 0,
x.label = "Distancia", y.label = "Actividad económica",
x.lim = c(-50, 50), binselect = "es", ci = 95,
title = "Discontinuidad en la actividad económica - 2013")
rdplot_2013 <- rdplot(y = dm2013, x = dist_p, c = 0, p = 4, binselect = "esmv",
x.label = "Distancia", y.label = "Actividad económica", y.lim = c(0, 2),
lowerend = -6000, upperend = 6000, col.lines = c(size = 10),
title = "Discontinuidad en la actividad económica - 2013")
detach(merge_rasters_dataframes)