rdrobust: Local-polynomial RD estimation with robust confidence intervals

Description

rdrobust implements a local-polynomial RD estimator with robust confidence intervals as proposed in Calonico, Cattaneo and Titiunik (2014a). It also computes alternative procedures available in the literature. Calonico, Cattaneo and Titiunik (2014b) provides an introduction to this command. Additional details for conventional approaches to conduct inference in the RD design can be found in Imbens and Lemieux (2008), Lee and Lemieux (2010), Dinardo and Lee (2011), and references therein. A companion Stata package is described in Calonico, Cattaneo and Titiunik (2014c).

Usage

rdrobust(y, x, data, subset = NULL, c = 0,  p = 1, q = 2, deriv = 0, 
            fuzzy = NULL, h = NULL, b = NULL, rho = NULL, scalepar = 1,
            kernel = "tri", bwselect = "CCT",  scaleregul = 1, delta = 0.5, 
            cvgrid_min = NULL, cvgrid_max = NULL, cvgrid_length = NULL, 
            cvplot = FALSE, vce = "nn", matches = 3, level = 95,
            all = FALSE, model = FALSE, frame = FALSE)

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., Cattaneo, M. D., and R. Titiunik. 2014a. "Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs." Econometrica, forthcoming. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Titiunik_2014_ECMA.pdf. Calonico, S., Cattaneo, M. D., and R. Titiunik. 2014b. "rdrobust: An R Package for Robust Inference in Regression-Discontinuity Designs." University of Michigan, Department of Economics. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Titiunik_2014_Rpkg.pdf. Calonico, S., Cattaneo, M. D., and R. Titiunik. 2014c. "Robust Data-Driven Inference in the Regression-Discontinuity Design." University of Michigan, Department of Economics. http://www-personal.umich.edu/~cattaneo/papers/Calonico-Cattaneo-Titiunik_2014_Stata.pdf. Dinardo, J., and D. S. Lee. 2011. "Program Evaluation and Research Designs." In Handbook of Labor Economics, ed. O. Ashenfelter and D. Card, vol. 4A, 463-536. Elsevier Science B.V. Imbens, G., and T. Lemieux. 2008. "Regression Discontinuity Designs: A Guide to Practice." Journal of Econometrics 142(2): 615-635. Imbens, G. W., and K. Kalyanaraman. 2012. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator." Review of Economic Studies 79(3): 933-959. Lee, D. S., and T. Lemieux. 2010. "Regression Discontinuity Designs in Economics." Journal of Economic Literature 48(2): 281-355. Ludwig, J., and D. L. Miller. 2007. "Does Head Start Improve Children's Life Chances? Evidence from a Regression Discontinuity Design." Quarterly Journal of Economics 122(1): 159-208.

Examples

Run this code

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