In April 2012, I wrote this blog post demonstrating an approach proposed in Lewbel (2012) that identifies endogenous regressor coefficients in a linear triangular system. Now I am happy to announce the release of the ivlewbel package, which contains a function through which Lewbel’s method can be applied in R. This package is now available to download on the CRAN.

Please see the example from the previous blog post replicated in the below. Additionally, it would be very helpful if people could comment on bugs and additional features they would like to add to the package. My contact details are in the about section of the blog.

library(ivlewbel)
beta1 <- beta2 <- NULL
for(k in 1:500){
#generate data (including intercept)
x1 <- rnorm(1000,0,1)
x2 <- rnorm(1000,0,1)
u <- rnorm(1000,0,1)
s1 <- rnorm(1000,0,1)
s2 <- rnorm(1000,0,1)
ov <- rnorm(1000,0,1)
e1 <- u + exp(x1)*s1 + exp(x2)*s1
e2 <- u + exp(-x1)*s2 + exp(-x2)*s2
y1 <- 1 + x1 + x2 + ov + e2
y2 <- 1 + x1 + x2 + y1 + 2*ov + e1
x3 <- rep(1,1000)
dat <- data.frame(y1,y2,x3,x1,x2)
#record ols estimate
beta1 <- c(beta1,coef(lm(y2~x1+x2+y1))[4])
#init values for iv-gmm
beta2 <- c(beta2,lewbel(formula = y2 ~ y1 | x1 + x2 | x1 + x2, data = dat)$coef.est[1,1])
}
library(sm)
d <- data.frame(rbind(cbind(beta1,"OLS"),cbind(beta2,"IV-GMM")))
d$beta1 <- as.numeric(as.character(d$beta1))
sm.density.compare(d$beta1, d$V2,xlab=("Endogenous Coefficient"))
title("Lewbel and OLS Estimates")
legend("topright", levels(d$V2),lty=c(1,2,3),col=c(2,3,4),bty="n")
abline(v=1)

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** DiffusePrioR » R**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as:

Data science,

Big Data, R jobs, visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...