R/BigVAR.R

In BigVAR: Dimension Reduction Methods for Multivariate Time Series

#' Dimension Reduction Methods for Multivariate Time Series.
#'
#' BigVAR implements the HLAG and VARX-L frameworks which allow for the estimation of vector autoregressions and vector autoregressions with exogenous variables using structured convex penalties.  This package originated as a 2014 Google "Summer of Code" Project.  The development version of this package is hosted on github: \url{https://github.com/wbnicholson/BigVAR}.
#'
#' @author Will Nicholson \email{wbn8@@cornell.edu},

#' @docType package
#' @useDynLib BigVAR 
#' @name BigVAR
#' @details To use the facilities of this package, starting with an \eqn{T \times k+m} multivariate time series (in which T denotes the length of the series, k the number of endogenous or "model") and run \code{\link{constructModel}} to create an object of class \code{\link{BigVAR}}.  \code{\link{cv.BigVAR}} creates an object of class \code{\link{BigVAR.results}}, which chooses an optimal penalty parameter based on minimizing h-step ahead forecasts on a specified cross-validation period over a grid of values as well as comparisons against AIC, BIC, unconditional mean, and a random walk.  There are plot functions for both BigVAR (\code{\link{plot.BigVAR}}) and BigVAR.results (\code{\link{plot}}) as well as a predict function for BigVAR.results (\code{\link{predict}}).
#' @seealso \code{\link{constructModel}}, \code{\link{cv.BigVAR}}, \code{\link{BigVAR.results}}, \code{\link{plot}}, \code{\link{predict}}
#' @examples
#' # Fit a Basic VAR-L(3,4) on simulated data
#' data(Y)
#' T1=floor(nrow(Y)/3)
#' T2=floor(2*nrow(Y)/3)
#' m1=constructModel(Y,p=4,struct="Basic",gran=c(50,10),verbose=FALSE,T1=T1,T2=T2,IC=FALSE)
#' plot(m1)
#' results=cv.BigVAR(m1)
#' plot(results)
#' predict(results,n.ahead=1)
#' @references
#' Lutkepohl "New Introduction to Multivariate Time Series",
#' Banbura, Marta, Domenico Giannone, and Lucrezia Reichlin. 'Large Bayesian vector auto regressions.' Journal of Applied Econometrics 25.1 (2010): 71-92.
#' Breheny P, Huang J (2011). “Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection.” Annals of Applied Statistics, 5(1), 232–253.
#' Nicholson, William, I. Wilms, J. Bien, and D. S. Matteson. High dimensional forecasting via interpretable vector autoregression. Journal of Machine Learning Research, 21(166):1–52, 2020.
#' William B. Nicholson, David S. Matteson, Jacob Bien,VARX-L: Structured regularization for large vector autoregressions with exogenous variables, International Journal of Forecasting, Volume 33, Issue 3, 2017, Pages 627-651,
#' William B Nicholson, David S. Matteson, and Jacob Bien (2016), 'BigVAR: Tools for Modeling Sparse High-Dimensional Multivariate Time Series' arxiv:1702.07094
#' @import Rcpp
#' @import methods
#' @import stats
#' @importFrom utils head
#' @importFrom utils setTxtProgressBar
#' @importFrom utils txtProgressBar
#' @importFrom utils tail


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