BigVAR {BigVAR}R Documentation

Dimension Reduction Methods for Multivariate Time Series.

Description

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.

Details

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).

Author(s)

Will Nicholson wbn8@cornell.edu,

References

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.

See Also

constructModel, cv.BigVAR, BigVAR.results, plot, 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)

[Package BigVAR version 1.0.6 Index]