https://github.com/cran/validann
Tip revision: 5ecf0a5f60a491a13ac0139a606046292eb9a413 authored by Greer B. Humphrey on 20 April 2017, 07:35:10 UTC
version 1.2.1
version 1.2.1
Tip revision: 5ecf0a5
ar9.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ar9.R
\docType{data}
\name{ar9}
\alias{ar9}
\title{Data generated by autoregressive AR9 model.}
\format{A data frame with 1000 rows and 16 variables:
\describe{
\item{x_t-1,x_t-2,x_t-3,x_t-4,x_t-5,x_t-6,x_t-7,x_t-8,x_t-9,x_t-10,x_t-11,x_t-12,}{}
\item{x_t-13,x_t-14,x_t-15}{lagged values of
x_t in columns 1:15}
\item{x_t}{dependent variable in column 16}
}}
\usage{
ar9
}
\description{
Synthetically generated dataset containing values of dependent variable
\code{x_t} given values of \cr
\code{x_t-1, x_t-2, ..., x_t-15}.
}
\details{
This dataset was generated using the AR9 model first described in
Sharma (2000) and given by:
\eqn{x_{t} = 0.3x_{t-1} - 0.6x_{t-4} - 0.5x_{t-9} + \epsilon_{t}}{%
x_t = 0.3x_t-1 - 0.6x_t-4 - 0.5x_t-9 + \epsilon_t}
where \eqn{\epsilon_{t}}{\epsilon_t}
}
\references{
Sharma, A. (2000), Seasonal to interannual rainfall
probabilistic forecasts for improved water supply management: Part 1 -
a strategy for system predictor identification, Journal of Hydrology,
239(1-4), 232-239, \url{http://dx.doi.org/10.1016/S0022-1694(00)00346-2}.
}
\keyword{datasets}