BigVAR (version 1.1.2)

BigVAR: Dimension Reduction Methods for Multivariate Time Series.

Description

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: https://github.com/wbnicholson/BigVAR.

Author

Will Nicholson ,

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).

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

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)