rdrobust: Local-Polynomial RD Estimation with Robust Confidence Intervals

Description

rdrobust implements local polynomial Regression Discontinuity (RD) point estimators with robust bias-corrected confidence intervals and inference procedures developed in Calonico, Cattaneo and Titiunik (2014a), Calonico, Cattaneo and Farrell (2016a), and Calonico, Cattaneo, Farrell and Titiunik (2016). It also computes alternative estimation and inference procedures available in the literature. Companion commands are: rdbwselect for data-driven bandwidth selection, and rdplot for data-driven RD plots (see Calonico, Cattaneo and Titiunik (2015a) for details). A detailed introduction to this command is given in Calonico, Cattaneo and Titiunik (2015b), and Calonico, Cattaneo, Farrell and Titiunik (2016b). A companion Stata package is described in Calonico, Cattaneo and Titiunik (2014b). For more details, and related Stata and R packages useful for analysis of RD designs, visit https://sites.google.com/site/rdpackages/

Usage

rdrobust(y, x, covs = NULL, fuzzy = NULL, cluster = NULL, 
            c = 0,  p = 1, q = 2, deriv = 0, 
            h = NULL, b = NULL, rho = NULL, scalepar = 1,
            kernel = "tri", bwselect = "mserd",  scaleregul = 1, sharpbw = FALSE, 
            vce = "nn", nnmatch = 3, level = 95, all = FALSE, subset = NULL)

Arguments

Value

ccutoff value.N_lsample size used to the left of the cutoff.N_rsample size used to the right of the cutoff.Noverall sample size.porder of the polynomial used for estimation of the regression function.qorder of the polynomial used for estimation of the bias of the regression function.hbandwidth used for estimation of the regression function.bbandwidth used for estimation of the bias of the regression function estimator.coefvector containing conventional and bias-corrected local-polynomial RD estimates.sevector containing conventional and robust standard errors of the local-polynomial RD estimates.pvvector containing the p-values associated with conventional, bias-corrected and robust local-polynomial RD estimates.cimatrix containing the confidence intervals associated with conventional, bias-corrected and robust local-polynomial RD estimates.

References

Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2016. On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference. Working Paper. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Farrell_2016_wp.pdf. Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik. 2016a. Regression Discontinuity Designs using Covariates. Working Paper. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Farrell-Titiunik_2016_wp.pdf. Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik. 2016b. rdrobust: Software for Regression Discontinuity Designs. Working Paper. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Farrell-Titiunik_2016_Stata.pdf. Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014a. Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica 82(6): 2295-2326. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Titiunik_2014_ECMA.pdf. Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014b. Robust Data-Driven Inference in the Regression-Discontinuity Design. Stata Journal 14(4): 909-946. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Titiunik_2014_Stata.pdf. Calonico, S., M. D. Cattaneo, and R. Titiunik. 2015a. Optimal Data-Driven Regression Discontinuity Plots. Journal of the American Statistical Association 110(512): 1753-1769. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Titiunik_2015_JASA.pdf. Calonico, S., M. D. Cattaneo, and R. Titiunik. 2015b. rdrobust: An R Package for Robust Nonparametric Inference in Regression-Discontinuity Designs. R Journal 7(1): 38-51. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Titiunik_2015_R.pdf. Cattaneo, M. D., B. Frandsen, and R. Titiunik. 2015. Randomization Inference in the Regression Discontinuity Design: An Application to the Study of Party Advantages in the U.S. Senate. Journal of Causal Inference 3(1): 1-24. http://www-personal.umich.edu/~cattaneo/papers/Cattaneo-Frandsen-Titiunik_2015_JCI.pdf.

Examples

Run this code

x<-runif(1000,-1,1)
y<-5+3*x+2*(x>=0)+rnorm(1000)
rdrobust(y,x)