BigVAR {BigVAR} | R Documentation |
BigVAR implements the HVAR 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: http://www.github.com/wbnicholson/BigVAR.
To use the facilities of this package, starting with an 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 constructModel
to create an object of class BigVAR
. cv.BigVAR
creates an object of class 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 (plot.BigVAR
) and BigVAR.results (plot
) as well as a predict function for BigVAR.results (predict
).
Will Nicholson wbn8@cornell.edu,
Lutkepohl "New Introduction to Multivariate Time Series", William B Nicholson, Jacob Bien, and David S Matteson. "High Dimensional Forecasting via Interpretable Vector Autoregression." arXiv preprint arXiv:1412.5250, 2016. William B Nicholson, David S. Matteson, and Jacob Bien (2015), "VARX-L Structured regularization for large vector autoregressions with exogenous variables," arXiv preprint arXiv:1508.07497, 2016.. William B Nicholson, David S. Matteson, and Jacob Bien (2016), "BigVAR: Dimension Reduction Reduction Methods for Multivariate Time Series," http://www.wbnicholson.com/BigVAR.pdf.
constructModel
, cv.BigVAR
, BigVAR.results
, plot
, predict
# 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)