R/stochvol-package.R

In gregorkastner/stochvol: Efficient Bayesian Inference for Stochastic Volatility (SV) Models

#  #####################################################################################
#  R package stochvol by
#     Gregor Kastner Copyright (C) 2013-2018
#     Gregor Kastner and Darjus Hosszejni Copyright (C) 2019-
#
#  This file is part of the R package stochvol: Efficient Bayesian
#  Inference for Stochastic Volatility Models.
#
#  The R package stochvol is free software: you can redistribute it
#  and/or modify it under the terms of the GNU General Public License
#  as published by the Free Software Foundation, either version 2 or
#  any later version of the License.
#
#  The R package stochvol is distributed in the hope that it will be
#  useful, but WITHOUT ANY WARRANTY; without even the implied warranty
#  of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
#  General Public License for more details.
#
#  You should have received a copy of the GNU General Public License
#  along with the R package stochvol. If that is not the case, please
#  refer to <http://www.gnu.org/licenses/>.
#  #####################################################################################

#' Euro exchange rate data
#'
#' The data set contains the daily bilateral prices of one Euro in 23
#' currencies from January 3, 2000, until April 4, 2012. Conversions to New
#' Turkish Lira and Fourth Romanian Leu have been incorporated.
#'
#'
#' @name exrates
#' @docType data
#' @seealso \code{\link{svsample}}
#' @source ECB Statistical Data Warehouse (\url{https://sdw.ecb.europa.eu})
#' @keywords datasets
#' @examples
#'
#' \dontrun{
#' data(exrates)
#' dat <- logret(exrates$USD, demean = TRUE)  ## de-meaned log-returns
#' res <- svsample(dat)                       ## run MCMC sampler
#' plot(res, forecast = 100)                  ## display results
#' }
#'
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#' Efficient Bayesian Inference for Stochastic Volatility (SV) Models
#'
#' This package provides an efficient algorithm for fully Bayesian estimation
#' of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC)
#' methods. Methodological details are given in Kastner and Frühwirth-Schnatter
#' (2014); the most common use cases are described in Kastner (2016). Recently,
#' the package has been extended to allow for the leverage effect.
#'
#' Bayesian inference for stochastic volatility models using MCMC methods
#' highly depends on actual parameter values in terms of sampling efficiency.
#' While draws from the posterior utilizing the standard centered
#' parameterization break down when the volatility of volatility parameter in
#' the latent state equation is small, non-centered versions of the model show
#' deficiencies for highly persistent latent variable series. The novel
#' approach of ancillarity-sufficiency interweaving (Yu and Meng, 2011) has
#' recently been shown to aid in overcoming these issues for a broad class of
#' multilevel models. This package provides software for ``combining best of
#' different worlds'' which allows for inference for parameter constellations
#' that have previously been infeasible to estimate without the need to select
#' a particular parameterization beforehand.
#'
#' @name stochvol-package
#' @aliases stochvol-package stochvol
#' @docType package
#' @useDynLib stochvol, .registration = TRUE
#' @importFrom utils tail head flush.console
#' @importFrom graphics plot par hist mtext lines title matplot points abline layout plot.default axis boxplot
#' @importFrom grDevices col2rgb rgb
#' @importFrom stats cov rt rgamma rnorm sd IQR density time lowess dnorm dbeta dgamma dexp qnorm qt ppoints ts.plot median quantile predict plot.ts qqline qqnorm qqplot coefficients lm .getXlevels delete.response model.frame model.matrix na.pass
#' @importFrom coda mcmc nvar niter varnames traceplot mcmc.list nvar nchain effectiveSize mcpar
#' @importFrom Rcpp sourceCpp
#' @importFrom parallel parLapply stopCluster clusterSetRNGStream makePSOCKcluster mclapply makeCluster clusterExport clusterEvalQ
#' @note This package is currently in active development. Your comments,
#' suggestions and requests are warmly welcome!
#' @author Gregor Kastner \email{gregor.kastner@@wu.ac.at}, Darjus Hosszejni \email{darjus.hosszejni@@wu.ac.at}
#' @references Kastner, G. and Frühwirth-Schnatter, S. (2014).
#' Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC
#' Estimation of Stochastic Volatility Models. \emph{Computational Statistics &
#' Data Analysis}, \bold{76}, 408--423,
#' \doi{10.1016/j.csda.2013.01.002}.
#'
#' Kastner, G. (2016). Dealing with Stochastic Volatility in Time Series Using the R Package stochvol.
#' \emph{Journal of Statistical Software}, \bold{69}(5), 1--30,
#' \doi{10.18637/jss.v069.i05}.
#'
#' Yu, Y. and Meng, X.-L. (2011). To Center or Not to Center: That is Not the
#' Question---An Ancillarity-Suffiency Interweaving Strategy (ASIS) for
#' Boosting MCMC Efficiency. \emph{Journal of Computational and Graphical
#' Statistics}, \bold{20}(3), 531--570,
#' \doi{10.1198/jcgs.2011.203main}.
#' @keywords package models ts
#' @example inst/examples/stochvol-package.R
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