% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/half_panel_jk.R
\name{.half_panel_jackknife}
\alias{.half_panel_jackknife}
\title{Half-Panel Jackknife Bias Correction}
\usage{
.half_panel_jackknife(panel_list, coef_names, fast = TRUE)
}
\arguments{
\item{panel_list}{Named list of \code{list(y, X)} pairs (from the
main \code{dcce()} pipeline).}

\item{coef_names}{Character vector: structural coefficient names to
extract from each half-sample fit.}

\item{fast}{Logical: use the C++ fast path.}
}
\value{
A named numeric vector of the half-panel MG average (to be
combined with the full-sample estimate in \code{dcce()}), or
\code{NULL} if the correction cannot be applied.
}
\description{
Implements the Chudik & Pesaran (2015) half-panel jackknife for
dynamic CCE/DCCE estimators. Each unit's time series is split into
two halves at the midpoint; MG estimates are computed on each half,
and the full-sample MG is bias-corrected as
\deqn{\hat\beta_{HPJ} = 2\hat\beta_{full}
  - \frac{1}{2}(\hat\beta_{half1} + \hat\beta_{half2}).}
}
\details{
This targets the Nickell (1981) bias that arises in short-T dynamic
panels when the lagged dependent variable is correlated with the
unit fixed effect.
}
\keyword{internal}
