R/ets.R

In bnRep: A Repository of Bayesian Networks from the Academic Literature

#' ets Bayesian Network
#'
#'
#' Uncovering drivers of EU carbon futures with Bayesian networks.
#'
#' @usage NULL
#'
#' @format
#' A discrete Bayesian network to model the influence of financial, economic, and energy-related factors on EUA futures prices. The model was given in the referenced paper. The vertices are:
#' \describe{
#' \item{CAC}{(High, Low, Neutral);}
#' \item{CO1}{(High, Low, Neutral);}
#' \item{DAX}{(High, Low, Neutral);}
#' \item{ECO}{(High, Low, Neutral);}
#' \item{EURCHF}{(High, Low, Neutral);}
#' \item{EURCNY}{(High, Low, Neutral);}
#' \item{EURGBP}{(High, Low, Neutral);}
#' \item{EURRUB}{(High, Low, Neutral);}
#' \item{EURUSD}{(High, Low, Neutral);}
#' \item{LBEATREU}{(High, Low, Neutral);}
#' \item{LB01TREU}{(High, Low, Neutral);}
#' \item{MO1}{(High, Low, Neutral);}
#' \item{MXEU0EN}{(High, Low, Neutral);}
#' \item{NG1COMB}{(High, Low, Neutral);}
#' \item{SPGTCED}{(High, Low, Neutral);}
#' \item{SPX}{(High, Low, Neutral);}
#' \item{SXXP}{(High, Low, Neutral);}
#' \item{VIX}{(High, Low, Neutral);}
#' \item{XA1}{(High, Low, Neutral);}
#' \item{XAU}{(High, Low, Neutral);}
#'  }
#'
#' @return An object of class \code{bn.fit}. Refer to the documentation of \code{bnlearn} for details.
#' @keywords NULL
#' @importClassesFrom bnlearn bn.fit
#' @references Maciejowski, J., & Leonelli, M. (2025). Uncovering Drivers of EU Carbon Futures with Bayesian Networks. arXiv preprint arXiv:2505.10384.
"ets"