RDHonestBME | R Documentation |
Computes honest CIs for local polynomial regression with uniform kernel in sharp RD under the assumption that the conditional mean lies in the bounded misspecification error (BME) class of functions, as considered in Kolesár and Rothe (2018). This class formalizes the notion that the fit of the chosen model is no worse at the cutoff than elsewhere in the estimation window.
RDHonestBME(
formula,
data,
subset,
cutoff = 0,
na.action,
h = Inf,
alpha = 0.05,
order = 0,
regformula
)
formula |
object of class |
data |
optional data frame, list or environment (or object coercible by
|
subset |
optional vector specifying a subset of observations to be used in the fitting process. |
cutoff |
specifies the RD cutoff in the running variable. |
na.action |
function which indicates what should happen when the data
contain |
h |
bandwidth, a scalar parameter. |
alpha |
determines confidence level, |
order |
Order of local regression |
regformula |
Explicitly specify regression formula to use instead of
running a local polynomial regression, with |
An object of class "RDResults"
. This is a list with at least
the following elements:
"coefficients"
Data frame containing estimation results, including point estimate, one- and two-sided confidence intervals, a bound on worst-case bias, bandwidth used, and the number of effective observations.
"call"
The matched call.
"lm"
An "lm"
object containing the fitted
regression.
"na.action"
(If relevant) information on the special
handling of NA
s.
subset
is evaluated in the same way as variables in formula
,
that is first in data
and then in the environment of formula
.
Michal Kolesár and Christoph Rothe. Inference in regression discontinuity designs with a discrete running variable. American Economic Review, 108(8):2277—-2304, August 2018. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1257/aer.20160945")}
RDHonestBME(log(earnings)~yearat14, data=cghs, h=3,
order=1, cutoff=1947)
## Equivalent to
RDHonestBME(log(earnings)~yearat14, data=cghs, h=3,
cutoff=1947, order=1, regformula="y~x*I(x>=0)")
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