bigtime {bigtime} | R Documentation |

## bigtime: A package for obtaining sparse estimates of large time series models.

### Description

The bigtime package provides sparse estimators for three large time series models: Vector AutoRegressive Models, Vector AutoRegressive Models with Exogenous variables, and Vector AutoRegressive Moving Average Models. The univariate cases are also supported.

### Details

To use the facilities of this package, start with a T by k time series matrix Y (for the VAR and VARMA), and an exogenous time series matrix X (for the VARX). Run sparseVAR, sparseVARX or sparseVARMA to get the estimated model. The function lagmatrix returns the lag matrix of estimated coefficients of the estimated model. The function directforecast gives h-step ahead forecasts based on the estimated model. The function recursiveforecast can be used to recursively forecast a VAR model. The function is.stable returns whether an estimated VAR model is stable. The function diagnostics_plot returns a plot of the fitted vs. observed values as well as of the residuals. The functions fitted and residuals return the fitted, respectively the residuals of the estimated model. The function simVAR can be used to simulate a VAR model with various sparsity patterns.

### Author(s)

Ines Wilms <i.wilms@maastrichtuniversity.nl>, Jacob Bien, David S. Matteson, Sumanta Basu, Will Nicholson, Enrico Wegner

### References

Nicholson William B., Wilms Ines, Bien Jacob and Matteson David S. (2020), “High-dimensional forecasting via interpretable vector autoregression”, Journal of Machine Learning Research, 21(166), 1-52.

Wilms Ines, Sumanta Basu, Bien Jacob and Matteson David S. (2021), “Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages”, Journal of the American Statistical Association, doi: 10.1080/01621459.2021.1942013.

### Examples

```
# Fit a sparse VAR model
data(var.example)
VARfit <- sparseVAR(Y=scale(Y.var), selection = "cv") # using time series cross-validation
Lhat <- lagmatrix(fit=VARfit) # get estimated lagmatrix
VARforecast <- directforecast(fit=VARfit, h=1) # get one-step ahead forecasts
```

*bigtime*version 0.2.3 Index]