rdd_reg_np: Parametric polynomial estimator of the regression...

In rddtools: Toolbox for Regression Discontinuity Design ('RDD')

Description

Compute a parametric polynomial regression of the ATE, possibly on the range specified by bandwidth

Usage

rdd_reg_np(
  rdd_object,
  covariates = NULL,
  bw = rdd_bw_ik(rdd_object),
  slope = c("separate", "same"),
  inference = c("np", "lm"),
  covar.opt = list(slope = c("same", "separate"), bw = NULL)
)

Arguments

rdd_object	Object of class rdd_data created by rdd_data
covariates	TODO
bw	A bandwidth to specify the subset on which the parametric regression is estimated
slope	Whether slopes should be different on left or right (separate), or the same.
inference	Type of inference to conduct: non-parametric one (np) or standard (lm). See details.
covar.opt	Options for the inclusion of covariates. Way to include covariates, either in the main regression (include) or as regressors of y in a first step (residual).

Value

An object of class rdd_reg_np and class lm, with specific print and plot methods

References

TODO

See Also

rdd_bw_ik Bandwidth selection using the plug-in bandwidth of Imbens and Kalyanaraman (2012)

Examples

## Step 0: prepare data
data(house)
house_rdd <- rdd_data(y=house$y, x=house$x, cutpoint=0)
## Step 2: regression
# Simple polynomial of order 1:
reg_nonpara <- rdd_reg_np(rdd_object=house_rdd)
print(reg_nonpara)
plot(reg_nonpara)

Example output

Loading required package: AER
Loading required package: car
Loading required package: carData
Loading required package: lmtest
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: survival
Loading required package: np
Nonparametric Kernel Methods for Mixed Datatypes (version 0.60-9)
[vignette("np_faq",package="np") provides answers to frequently asked questions]
[vignette("np",package="np") an overview]
[vignette("entropy_np",package="np") an overview of entropy-based methods]
### RDD regression: nonparametric local linear###
	Bandwidth:  0.2938561 
	Number of obs: 3200 (left: 1594, right: 1606)

	Coefficient:
  Estimate Std. Error z value  Pr(>|z|)    
D 0.079924   0.009465  8.4443 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

rddtools documentation built on Jan. 10, 2022, 5:07 p.m.