Title: | Robust Data-Driven Statistical Inference in Regression-Discontinuity Designs |
---|---|
Description: | Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots). |
Authors: | Sebastian Calonico <[email protected]>, Matias D. Cattaneo <[email protected]>, Max H. Farrell <[email protected]>, Rocio Titiunik <[email protected]> |
Maintainer: | Sebastian Calonico <[email protected]> |
License: | GPL-2 |
Version: | 2.2 |
Built: | 2024-12-19 06:27:15 UTC |
Source: | CRAN |
Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust
to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect
to perform bandwidth selection for the different procedures implemented, and rdplot
to conduct exploratory data analysis (RD plots).
Package: | rdrobust |
Type: | Package |
Version: | 2.2 |
Date: | 2023-11-03 |
License: | GPL-2 |
Function for statistical inference: rdrobust
Function for bandwidths selection: rdbwselect
Function for exploratory data analysis (RD plots): rdplot
Sebastian Calonico, Columbia University, New York, NY. [email protected].
Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].
Max H. Farrell, University of California, Santa Barbara, CA. [email protected].
Rocio Titiunik, Princeton University, Princeton, NJ. [email protected].
rdbwselect
implements bandwidth selectors for local polynomial Regression Discontinuity (RD) point estimators and inference procedures developed in Calonico, Cattaneo and Titiunik (2014a), Calonico, Cattaneo and Farrell (2018), Calonico, Cattaneo, Farrell and Titiunik (2019) and Calonico, Cattaneo and Farrell (2020).
Companion commands are: rdrobust
for point estimation and inference procedures, 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 (2019). 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://rdpackages.github.io/
rdbwselect(y, x, c = NULL, fuzzy = NULL, deriv = NULL, p = NULL, q = NULL, covs = NULL, covs_drop = TRUE, ginv.tol = 1e-20, kernel = "tri", weights = NULL, bwselect = "mserd", vce = "nn", cluster = NULL, nnmatch = 3, scaleregul = 1, sharpbw = FALSE, all = NULL, subset = NULL, masspoints = "adjust", bwcheck = NULL, bwrestrict = TRUE, stdvars = FALSE)
rdbwselect(y, x, c = NULL, fuzzy = NULL, deriv = NULL, p = NULL, q = NULL, covs = NULL, covs_drop = TRUE, ginv.tol = 1e-20, kernel = "tri", weights = NULL, bwselect = "mserd", vce = "nn", cluster = NULL, nnmatch = 3, scaleregul = 1, sharpbw = FALSE, all = NULL, subset = NULL, masspoints = "adjust", bwcheck = NULL, bwrestrict = TRUE, stdvars = FALSE)
y |
is the dependent variable. |
x |
is the running variable (a.k.a. score or forcing variable). |
c |
specifies the RD cutoff in |
fuzzy |
specifies the treatment status variable used to implement fuzzy RD estimation (or Fuzzy Kink RD if |
deriv |
specifies the order of the derivative of the regression functions to be estimated. Default is |
p |
specifies the order of the local-polynomial used to construct the point-estimator; default is |
q |
specifies the order of the local-polynomial used to construct the bias-correction; default is |
covs |
specifies additional covariates to be used for estimation and inference. |
covs_drop |
if TRUE, it checks for collinear additional covariates and drops them. Default is TRUE. |
ginv.tol |
tolerance used to invert matrices involving covariates when |
kernel |
is the kernel function used to construct the local-polynomial estimator(s). Options are |
weights |
is the variable used for optional weighting of the estimation procedure. The unit-specific weights multiply the kernel function. |
bwselect |
specifies the bandwidth selection procedure to be used. Options are:
Note: MSE = Mean Square Error; CER = Coverage Error Rate.
Default is |
vce |
specifies the procedure used to compute the variance-covariance matrix estimator. Options are:
Default is |
cluster |
indicates the cluster ID variable used for cluster-robust variance estimation with degrees-of-freedom weights. By default it is combined with |
nnmatch |
to be combined with for |
scaleregul |
specifies scaling factor for the regularization term added to the denominator of the bandwidth selectors. Setting |
sharpbw |
option to perform fuzzy RD estimation using a bandwidth selection procedure for the sharp RD model. This option is automatically selected if there is perfect compliance at either side of the threshold. |
all |
if specified, |
subset |
an optional vector specifying a subset of observations to be used. |
masspoints |
checks and controls for repeated observations in the running variable. Options are: (i) (ii) (iii) Default option is |
bwcheck |
if a positive integer is provided, the preliminary bandwidth used in the calculations is enlarged so that at least |
bwrestrict |
if |
stdvars |
if |
N |
vector with sample sizes to the left and to the righst of the cutoff. |
c |
cutoff value. |
p |
order of the local-polynomial used to construct the point-estimator. |
q |
order of the local-polynomial used to construct the bias-correction estimator. |
bws |
matrix containing the estimated bandwidths for each selected procedure. |
bwselect |
bandwidth selection procedure employed. |
kernel |
kernel function used to construct the local-polynomial estimator(s). |
Sebastian Calonico, Columbia University, New York, NY. [email protected].
Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].
Max H. Farrell, University of California, Santa Barbara, CA. [email protected].
Rocio Titiunik, Princeton University, Princeton, NJ. [email protected].
Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2018. On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference. Journal of the American Statistical Association, 113(522): 767-779.
Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2020. Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs. Econometrics Journal, 23(2): 192-210.
Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik. 2017. rdrobust: Software for Regression Discontinuity Designs. Stata Journal 17(2): 372-404.
Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik. 2019. Regression Discontinuity Designs using Covariates. Review of Economics and Statistics, 101(3): 442-451.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014a. Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica 82(6): 2295-2326.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014b. Robust Data-Driven Inference in the Regression-Discontinuity Design. Stata Journal 14(4): 909-946.
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.
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.
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.
x<-runif(1000,-1,1) y<-5+3*x+2*(x>=0)+rnorm(1000) rdbwselect(y,x)
x<-runif(1000,-1,1) y<-5+3*x+2*(x>=0)+rnorm(1000) rdbwselect(y,x)
rdbwselect_2014
is a deprecated command implementing three bandwidth selectors for local polynomial Regression Discontinuity (RD) point estimators and inference procedures, as described in Calonico, Cattaneo and Titiunik (2014).
This command is no longer supported or updated, and it is made available only for backward compatibility purposes. Please use rdbwselect
instead.
The latest version of the rdrobust package includes the following commands:
rdrobust
for point estimation and inference procedures.
rdbwselect
for data-driven bandwidth selection.
rdplot
for data-driven RD plots.
For more details, and related Stata and R packages useful for analysis of RD designs, visit https://rdpackages.github.io/
rdbwselect_2014(y, x, subset = NULL, c = 0, p = 1, q = 2, deriv = 0, rho = NULL, kernel = "tri", bwselect = "CCT", scaleregul = 1, delta = 0.5, cvgrid_min = NULL, cvgrid_max = NULL, cvgrid_length = NULL, cvplot = FALSE, vce = "nn", matches = 3, all = FALSE, precalc = TRUE )
rdbwselect_2014(y, x, subset = NULL, c = 0, p = 1, q = 2, deriv = 0, rho = NULL, kernel = "tri", bwselect = "CCT", scaleregul = 1, delta = 0.5, cvgrid_min = NULL, cvgrid_max = NULL, cvgrid_length = NULL, cvplot = FALSE, vce = "nn", matches = 3, all = FALSE, precalc = TRUE )
y |
is the dependent variable. |
x |
is the running variable (a.k.a. score or forcing variable). |
subset |
an optional vector specifying a subset of observations to be used. |
c |
specifies the RD cutoff in |
p |
specifies the order of the local-polynomial used to construct the point-estimator; default is |
q |
specifies the order of the local-polynomial used to construct the bias-correction; default is |
deriv |
specifies the order of the derivative of the regression function to be estimated; default is |
rho |
if specified, sets the pilot bandwidth |
kernel |
is the kernel function used to construct the local-polynomial estimator(s). Options are |
bwselect |
selects the bandwidth selection procedure to be used. By default it computes both
|
scaleregul |
specifies scaling factor for the regularization terms of |
delta |
sets the quantile that defines the sample used in the cross-validation procedure. This option is used only if |
cvgrid_min |
sets the minimum value of the bandwidth grid used in the cross-validation procedure. This option is used only if |
cvgrid_max |
sets the maximum value of the bandwidth grid used in the cross-validation procedure. This option is used only if |
cvgrid_length |
sets the bin length of the (evenly-spaced) bandwidth grid used in the cross-validation procedure. This option is used only if |
cvplot |
generates a graph of the CV objective function. This option is used only if |
vce |
specifies the procedure used to compute the variance-covariance matrix estimator. This option is used only if
|
matches |
specifies the number of matches in the nearest-neighbor based variance-covariance matrix estimator. This options is used only when nearest-neighbor matches residuals are employed; default is |
all |
if specified,
|
precalc |
internal option. |
bws |
matrix containing the estimated bandwidths for each selected procedure. |
bwselect |
bandwidth selection procedure employed. |
kernel |
kernel function used to construct the local-polynomial estimator(s). |
p |
order of the local-polynomial used to construct the point-estimator. |
q |
order of the local-polynomial used to construct the bias-correction estimator. |
Sebastian Calonico, Columbia University, New York, NY. [email protected].
Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].
Max H. Farrell, University of California, Santa Barbara, CA. [email protected].
Rocio Titiunik, Princeton University, Princeton, NJ. [email protected].
Calonico, S., Cattaneo, M. D., and R. Titiunik. 2014. Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica 82(6): 2295-2326. .
x<-runif(1000,-1,1) y<-5+3*x+2*(x>=0)+rnorm(1000) rdbwselect_2014(y,x)
x<-runif(1000,-1,1) y<-5+3*x+2*(x>=0)+rnorm(1000) rdbwselect_2014(y,x)
rdplot
implements several data-driven Regression Discontinuity (RD) plots, using either evenly-spaced or quantile-spaced partitioning. Two type of RD plots are constructed: (i) RD plots with binned sample means tracing out the underlying regression function, and (ii) RD plots with binned sample means mimicking the underlying variability of the data. For technical and methodological details see Calonico, Cattaneo and Titiunik (2015a).
Companion commands are: rdrobust
for point estimation and inference procedures, and rdbwselect
for data-driven bandwidth selection.
A detailed introduction to this command is given in Calonico, Cattaneo and Titiunik (2015b) and Calonico, Cattaneo, Farrell and Titiunik (2017). A companion Stata
package is described in Calonico, Cattaneo and Titiunik (2014).
For more details, and related Stata and R packages useful for analysis of RD designs, visit https://rdpackages.github.io/
rdplot(y, x, c = 0, p = 4, nbins = NULL, binselect = "esmv", scale = NULL, kernel = "uni", weights = NULL, h = NULL, covs = NULL, covs_eval = "mean", covs_drop = TRUE, ginv.tol = 1e-20, support = NULL, subset = NULL, masspoints = "adjust", hide = FALSE, ci = NULL, shade = FALSE, title = NULL, x.label = NULL, y.label = NULL, x.lim = NULL, y.lim = NULL, col.dots = NULL, col.lines = NULL)
rdplot(y, x, c = 0, p = 4, nbins = NULL, binselect = "esmv", scale = NULL, kernel = "uni", weights = NULL, h = NULL, covs = NULL, covs_eval = "mean", covs_drop = TRUE, ginv.tol = 1e-20, support = NULL, subset = NULL, masspoints = "adjust", hide = FALSE, ci = NULL, shade = FALSE, title = NULL, x.label = NULL, y.label = NULL, x.lim = NULL, y.lim = NULL, col.dots = NULL, col.lines = NULL)
y |
is the dependent variable. |
x |
is the running variable (a.k.a. score or forcing variable). |
c |
specifies the RD cutoff in |
p |
specifies the order of the global-polynomial used to approximate the population conditional mean functions for control and treated units; default is |
nbins |
specifies the number of bins used to the left of the cutoff, denoted |
binselect |
specifies the procedure to select the number of bins. This option is available only if
|
scale |
specifies a multiplicative factor to be used with the optimal numbers of bins selected. Specifically, the number of bins used for the treatment and control groups will be |
kernel |
specifies the kernel function used to construct the local-polynomial estimator(s). Options are: |
weights |
is the variable used for optional weighting of the estimation procedure. The unit-specific weights multiply the kernel function. |
h |
specifies the bandwidth used to construct the (global) polynomial fits given the kernel choice |
covs |
specifies additional covariates to be used in the polynomial regression. |
covs_eval |
sets the evaluation points for the additional covariates, when included in the estimation. Options are: |
covs_drop |
if TRUE, it checks for collinear additional covariates and drops them. Default is TRUE. |
ginv.tol |
tolerance used to invert matrices involving covariates when |
support |
specifies an optional extended support of the running variable to be used in the construction of the bins; default is the sample range. |
subset |
an optional vector specifying a subset of observations to be used. |
masspoints |
checks and controls for repeated observations in the running variable. Options are: (i) (ii) (iii) Default option is |
hide |
logical. If |
ci |
optional graphical option to display confidence intervals of selected level for each bin. |
shade |
optional graphical option to replace confidence intervals with shaded areas. |
title |
optional title for the RD plot. |
x.label |
optional label for the x-axis of the RD plot. |
y.label |
optional label for the y-axis of the RD plot. |
x.lim |
optional setting for the range of the x-axis in the RD plot. |
y.lim |
optional setting for the range of the y-axis in the RD plot. |
col.dots |
optional setting for the color of the dots in the RD plot. |
col.lines |
optional setting for the color of the lines in the RD plot. |
binselect |
method used to compute the optimal number of bins. |
N |
sample sizes used to the left and right of the cutoff. |
Nh |
effective sample sizes used to the left and right of the cutoff. |
c |
cutoff value. |
p |
order of the global polynomial used. |
h |
bandwidth used to the left and right of the cutoff. |
kernel |
kernel used. |
J |
selected number of bins to the left and right of the cutoff. |
J_IMSE |
IMSE optimal number of bins to the left and right of the cutoff. |
J_MV |
Mimicking variance number of bins to the left and right of the cutoff. |
coef |
matrix containing the coefficients of the |
coef_covs |
coefficients of the additional covariates, only returned when |
scale |
selected scale value. |
rscale |
implicit scale value. |
bin_avg |
average bin length. |
bin_med |
median bin length. |
vars_bins |
data frame containing the variables used to construct the bins: bin id, cutoff values, mean of x and y within each bin, cutoff points and confidence interval bounds. |
vars_poly |
data frame containing the variables used to construct the global polynomial plot. |
rdplot |
a standard |
Sebastian Calonico, Columbia University, New York, NY. [email protected].
Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].
Max H. Farrell, University of California, Santa Barbara, CA. [email protected].
Rocio Titiunik, Princeton University, Princeton, NJ. [email protected].
Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik. 2017. rdrobust: Software for Regression Discontinuity Designs. Stata Journal 17(2): 372-404.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014. Robust Data-Driven Inference in the Regression-Discontinuity Design. Stata Journal 14(4): 909-946.
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.
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.
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.
x<-runif(1000,-1,1) y<-5+3*x+2*(x>=0)+rnorm(1000) rdplot(y,x)
x<-runif(1000,-1,1) y<-5+3*x+2*(x>=0)+rnorm(1000) rdplot(y,x)
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 (2018), Calonico, Cattaneo, Farrell and Titiunik (2019), and Calonico, Cattaneo and Farrell (2020). 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 (2017). 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://rdpackages.github.io/
rdrobust(y, x, c = NULL, fuzzy = NULL, deriv = NULL, p = NULL, q = NULL, h = NULL, b = NULL, rho = NULL, covs = NULL, covs_drop = TRUE, ginv.tol = 1e-20, kernel = "tri", weights = NULL, bwselect = "mserd", vce = "nn", cluster = NULL, nnmatch = 3, level = 95, scalepar = 1, scaleregul = 1, sharpbw = FALSE, all = NULL, subset = NULL, masspoints = "adjust", bwcheck = NULL, bwrestrict = TRUE, stdvars = FALSE)
rdrobust(y, x, c = NULL, fuzzy = NULL, deriv = NULL, p = NULL, q = NULL, h = NULL, b = NULL, rho = NULL, covs = NULL, covs_drop = TRUE, ginv.tol = 1e-20, kernel = "tri", weights = NULL, bwselect = "mserd", vce = "nn", cluster = NULL, nnmatch = 3, level = 95, scalepar = 1, scaleregul = 1, sharpbw = FALSE, all = NULL, subset = NULL, masspoints = "adjust", bwcheck = NULL, bwrestrict = TRUE, stdvars = FALSE)
y |
is the dependent variable. |
x |
is the running variable (a.k.a. score or forcing variable). |
c |
specifies the RD cutoff in |
fuzzy |
specifies the treatment status variable used to implement fuzzy RD estimation (or Fuzzy Kink RD if |
deriv |
specifies the order of the derivative of the regression functions to be estimated. Default is |
p |
specifies the order of the local-polynomial used to construct the point-estimator; default is |
q |
specifies the order of the local-polynomial used to construct the bias-correction; default is |
h |
specifies the main bandwidth used to construct the RD point estimator. If not specified, bandwidth |
b |
specifies the bias bandwidth used to construct the bias-correction estimator. If not specified, bandwidth |
rho |
specifies the value of |
covs |
specifies additional covariates to be used for estimation and inference. |
covs_drop |
if TRUE, it checks for collinear additional covariates and drops them. Default is TRUE. |
ginv.tol |
tolerance used to invert matrices involving covariates when |
kernel |
is the kernel function used to construct the local-polynomial estimator(s). Options are |
weights |
is the variable used for optional weighting of the estimation procedure. The unit-specific weights multiply the kernel function. |
bwselect |
specifies the bandwidth selection procedure to be used. By default it computes both |
Options are:
mserd
one common MSE-optimal bandwidth selector for the RD treatment effect estimator.
msetwo
two different MSE-optimal bandwidth selectors (below and above the cutoff) for the RD treatment effect estimator.
msesum
one common MSE-optimal bandwidth selector for the sum of regression estimates (as opposed to difference thereof).
msecomb1
for min(mserd
,msesum
).
msecomb2
for median(msetwo
,mserd
,msesum
), for each side of the cutoff separately.
cerrd
one common CER-optimal bandwidth selector for the RD treatment effect estimator.
certwo
two different CER-optimal bandwidth selectors (below and above the cutoff) for the RD treatment effect estimator.
cersum
one common CER-optimal bandwidth selector for the sum of regression estimates (as opposed to difference thereof).
cercomb1
for min(cerrd
,cersum
).
cercomb2
for median(certwo
,cerrd
,cersum
), for each side of the cutoff separately.
Note: MSE = Mean Square Error; CER = Coverage Error Rate.
Default is bwselect=mserd
. For details on implementation see Calonico, Cattaneo and Titiunik (2014a), Calonico, Cattaneo and Farrell (2018), and Calonico, Cattaneo, Farrell and Titiunik (2019), and the companion software articles.
vce |
specifies the procedure used to compute the variance-covariance matrix estimator. Options are:
Default is |
cluster |
indicates the cluster ID variable used for cluster-robust variance estimation with degrees-of-freedom weights. By default it is combined with |
nnmatch |
to be combined with for |
level |
sets the confidence level for confidence intervals; default is |
scalepar |
specifies scaling factor for RD parameter of interest. This option is useful when the population parameter of interest involves a known multiplicative factor (e.g., sharp kink RD). Default is |
scaleregul |
specifies scaling factor for the regularization term added to the denominator of the bandwidth selectors. Setting |
sharpbw |
option to perform fuzzy RD estimation using a bandwidth selection procedure for the sharp RD model. This option is automatically selected if there is perfect compliance at either side of the cutoff. |
all |
if specified, (i) conventional RD estimates with conventional standard errors. (ii) bias-corrected estimates with conventional standard errors. (iii) bias-corrected estimates with robust standard errors. |
subset |
an optional vector specifying a subset of observations to be used. |
masspoints |
checks and controls for repeated observations in the running variable. Options are: (i) (ii) (iii) Default option is |
bwcheck |
if a positive integer is provided, the preliminary bandwidth used in the calculations is enlarged so that at least |
bwrestrict |
if |
stdvars |
if |
N |
vector with the sample sizes used to the left and to the right of the cutoff. |
N_h |
vector with the effective sample sizes used to the left and to the right of the cutoff. |
c |
cutoff value. |
p |
order of the polynomial used for estimation of the regression function. |
q |
order of the polynomial used for estimation of the bias of the regression function. |
bws |
matrix containing the bandwidths used. |
tau_cl |
conventional local-polynomial estimate to the left and to the right of the cutoff. |
tau_bc |
bias-corrected local-polynomial estimate to the left and to the right of the cutoff. |
coef |
vector containing conventional and bias-corrected local-polynomial RD estimates. |
se |
vector containing conventional and robust standard errors of the local-polynomial RD estimates. |
bias |
estimated bias for the local-polynomial RD estimator below and above the cutoff. |
beta_Y_p_l |
conventional p-order local-polynomial estimates to the left of the cutoff for the outcome variable. |
beta_Y_p_r |
conventional p-order local-polynomial estimates to the right of the cutoff for the outcome variable. |
beta_T_p_l |
conventional p-order local-polynomial estimates to the left of the cutoff for the first stage (fuzzy RD). |
beta_T_p_r |
conventional p-order local-polynomial estimates to the right of the cutoff for the first stage (fuzzy RD). |
beta_covs |
coefficients of the additional covariates, only returned when |
V_cl_l |
conventional variance-covariance matrix estimated below the cutoff. |
V_cl_r |
conventional variance-covariance matrix estimated above the cutoff. |
V_rb_l |
robust variance-covariance matrix estimated below the cutoff. |
V_rb_r |
robust variance-covariance matrix estimated above the cutoff. |
pv |
vector containing the p-values associated with conventional, bias-corrected and robust local-polynomial RD estimates. |
ci |
matrix containing the confidence intervals associated with conventional, bias-corrected and robust local-polynomial RD estimates. |
Sebastian Calonico, Columbia University, New York, NY. [email protected].
Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].
Max H. Farrell, University of California, Santa Barbara, CA. [email protected].
Rocio Titiunik, Princeton University, Princeton, NJ. [email protected].
Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2018. On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference. Journal of the American Statistical Association, 113(522): 767-779.
Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2020. Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs. Econometrics Journal, 23(2): 192-210.
Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik. 2017. rdrobust: Software for Regression Discontinuity Designs. Stata Journal, 17(2): 372-404.
Calonico, S., M. D. Cattaneo, M. H. Farrell, and R. Titiunik. 2019. Regression Discontinuity Designs using Covariates. Review of Economics and Statistics, 101(3): 442-451.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014a. Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica 82(6): 2295-2326.
Calonico, S., M. D. Cattaneo, and R. Titiunik. 2014b. Robust Data-Driven Inference in the Regression-Discontinuity Design. Stata Journal 14(4): 909-946.
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.
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.
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.
x<-runif(1000,-1,1) y<-5+3*x+2*(x>=0)+rnorm(1000) rdrobust(y,x)
x<-runif(1000,-1,1) y<-5+3*x+2*(x>=0)+rnorm(1000) rdrobust(y,x)
Extract of the dataset constructed by Cattaneo, Frandsen, and Titiunik (2015), which include measures of incumbency advantage in the U.S. Senate for the period 1914-2010.
data(rdrobust_RDsenate)
data(rdrobust_RDsenate)
A data frame with 1390 observations on the following 2 variables.
margin
a numeric vector.
vote
a numeric vector.
Cattaneo, M. D., Frandsen, B., 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.
Cattaneo, M. D., Frandsen, B., 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.