require(foreign, quietly = TRUE)
d <- read.dta("regdata0.dta")
summary(d$iwm94)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -1.0 -0.5 -0.3 -0.3 -0.1 1.0 544
with(d, plot(iwm94, hischshr1520f, pch = 19, cex = 0.2, xlim = c(-0.5, 0.5)))
left.lm <- lm(hischshr1520f ~ iwm94, d, subset = iwm94 < 0)
right.lm <- lm(hischshr1520f ~ iwm94, d, subset = iwm94 >= 0)
left.x <- seq(-0.5, 0, 0.01)
right.x <- -left.x
lines(left.x, predict(left.lm, newd = data.frame(iwm94 = left.x)), col = "red")
lines(right.x, predict(right.lm, newd = data.frame(iwm94 = right.x)), col = "red")
left.lm
and right.lm
.rdd
require(rdd, quietly = TRUE)
## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Loading required package: car
## Loading required package: survival
## Loading required package: splines
rd.out <- RDestimate(hischshr1520f ~ iwm94, d)
rd.out
##
## Call:
## RDestimate(formula = hischshr1520f ~ iwm94, data = d)
##
## Coefficients:
## LATE Half-BW Double-BW
## 0.0296 0.0250 0.0228
summary(rd.out)
##
## Call:
## RDestimate(formula = hischshr1520f ~ iwm94, data = d)
##
## Type:
## sharp
##
## Estimates:
## Bandwidth Observations Estimate Std. Error z value
## LATE 0.24 1020 0.0296 0.0124 2.39
## Half-BW 0.12 589 0.0250 0.0165 1.52
## Double-BW 0.48 2050 0.0228 0.0101 2.26
## Pr(>|z|)
## LATE 0.0169 *
## Half-BW 0.1286
## Double-BW 0.0240 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## F-statistics:
## F Num. DoF Denom. DoF p
## LATE 4.99 3 1016 3.86e-03
## Half-BW 1.70 3 585 3.30e-01
## Double-BW 25.77 3 2046 4.44e-16
plot(rd.out, range = c(-0.4, 0.4))
title(xlab = "Islamic Party Vote Margin", ylab = "Female High School Education Share")
# Age 19+
RDestimate(ageshr19 ~ iwm94, d)[c("est", "se")]
## $est
## LATE Half-BW Double-BW
## -0.003737 0.006946 -0.004117
##
## $se
## [1] 0.010314 0.013783 0.008307
# Log Population
RDestimate(lpop1994 ~ iwm94, d)[c("est", "se")]
## $est
## LATE Half-BW Double-BW
## 0.06921 -0.04339 0.03000
##
## $se
## [1] 0.2384 0.3276 0.1879
# Household Size
RDestimate(shhs ~ iwm94, d)[c("est", "se")]
## $est
## LATE Half-BW Double-BW
## -0.006963 0.321148 -0.091759
##
## $se
## [1] 0.3543 0.5431 0.2557
# Men in 2000
RDestimate(hischshr1520m ~ iwm94, d)[c("est", "se")]
## $est
## LATE Half-BW Double-BW
## 0.009632 0.016188 0.007619
##
## $se
## [1] 0.009037 0.011807 0.007435
# Women in 1990 (pre-treatment)
RDestimate(c90hischshr1520f ~ iwm94, d)[c("est", "se")]
## $est
## LATE Half-BW Double-BW
## 0.0079389 0.0007974 0.0130517
##
## $se
## [1] 0.012239 0.017631 0.009308
# Men in 1990 (pre-treatment)
RDestimate(c90hischshr1520m ~ iwm94, d)[c("est", "se")]
## $est
## LATE Half-BW Double-BW
## 0.005930 0.002779 0.003861
##
## $se
## [1] 0.009770 0.013259 0.007891
DCdensity(d$iwm94, verbose = TRUE, plot = FALSE)
## Assuming cutpoint of zero.
## Using calculated bin size: 0.009
## Using calculated bandwidth: 0.165
## Log difference in heights is -0.095 with SE 0.147
## this gives a z-stat of -0.650
## and a p value of 0.515
## [1] 0.5154
DCdensity(d$iwm94)
## [1] 0.5154
RDestimate(Y~runvar+treatment)
rdd
code.