Description Usage Arguments Value
Basic computing engine called by RDHonest
to compute honest
confidence intervals for local polynomial estimators.
1 2 3  RDHonest.fit(d, M, kern = "triangular", hp, hm = hp, opt.criterion,
bw.equal = TRUE, alpha = 0.05, beta = 0.8, se.method = "nn", J = 3,
sclass = "H", order = 1, se.initial = "IKEHW")

d 
object of class 
M 
Bound on second derivative of the conditional mean function. 
kern 
specifies kernel function used in the local regression. It can
either be a string equal to 
hp, hm 
bandwidth for treated (units with positive running variable),
and control (units with negative running variable) units. If 
opt.criterion 
Optimality criterion that bandwidth is designed to optimize. It can either be based on exact finitesample maximum bias and finitesample estimate of variance, or asymptotic approximations to the bias and variance. The options are:
The finitesample methods use conditional variance given by

bw.equal 
logical specifying whether bandwidths on either side of cutoff should be constrainted to equal to each other. 
alpha 
determines confidence level, 
beta 
Determines quantile of excess length to optimize, if bandwidth optimizes given quantile of excess length of onesided confidence intervals. 
se.method 
Vector with methods for estimating standard error of
estimate. If

J 
Number of nearest neighbors, if "nn" is specified in

sclass 
Smoothness class, either 
order 
Order of local regression 1 for linear, 2 for quadratic. 
se.initial 
Method for estimating initial variance for computing optimal bandwidth. Ignored if data already contains estimate of variance.

Returns an object of class "RDResults"
, see description in
RDHonest
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