% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{djia2002}
\alias{djia2002}
\title{Dow Jones Industrial Average Daily Data (July-December 2002)}
\format{
A data frame with 127 rows and 3 variables:
\describe{
\item{date}{Trading date}
\item{close}{DJIA closing price (USD)}
\item{change}{Daily change in closing price (first difference;
NA for the first observation)}
}
}
\source{
Yahoo Finance via the quantmod R package.
}
\usage{
djia2002
}
\description{
Daily closing prices and changes of the Dow Jones Industrial Average
for the second half of 2002. Used in published PMM2 research to
demonstrate AR(1) estimation with asymmetric innovations.
}
\details{
The daily changes exhibit positive skewness, making this dataset
suitable for PMM2 estimation. In the original paper, an AR(1) model
fitted to the change series yielded PMM2 coefficient a1 = -0.43
versus OLS a1 = -0.49, with g2 = 0.77 (23\\% variance reduction).
}
\examples{
data(djia2002)
# AR(1) with PMM2
changes <- na.omit(djia2002$change)
pmm_skewness(changes)  # positive skewness -> PMM2
fit <- ar_pmm2(changes, order = 1)
summary(fit)
}
\references{
Zabolotnii S., Tkachenko O., Warsza Z.L. (2022) Application of the
Polynomial Maximization Method for Estimation Parameters of Autoregressive
Models with Asymmetric Innovations. Springer AISC, vol 1427.
\doi{10.1007/978-3-031-03502-9_37}
}
\keyword{datasets}
