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]