tsgauss: Two Step Gaussian Estimator

In kota7/rddsigma: Sharp Regression Discontinuity Design with Running Variable Measured with Errors

Description

Estimate the standard deviation of measurement error in the running variable of sharp RDD. This estimator is constructed under the assumption that true running variable and measurement error follow the Gaussian distribution.

Usage

tsgauss(d_vec, w_vec, cutoff, init_sigma = NULL, ...)

Arguments

d_vec	binary integer vector of assignment
w_vec	numeric vector of observed running variable
cutoff	threshold value for assignment
init_sigma	initial value of sigma. If NULL, randomly assigned
...	additional controls for optim

Value

object of rddsigma class

References

Kevin M. Murphy and Robert H. Topel (1985), Estimation and Inference in Two-Step Econometric Models. Journal of Business & Economic Statistics, 3(4), pp.370-379

Examples

set.seed(123)
dat <- gen_data(500, 0.2, 0)
tsgauss(dat$d, dat$w, 0)

kota7/rddsigma documentation built on May 20, 2019, 1:11 p.m.