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.

Author(s)

Ines Wilms <ines.wilms@kuleuven.be>, Jacob Bien, David S. Matteson, Sumanta Basu

References

Nicholson William B., Bien Jacob and Matteson David S. (2017), "High Dimensional Forecasting via Interpretable Vector Autoregression" arXiv preprint <arXiv:1412.5250v2>.

Wilms Ines, Sumanta Basu, Bien Jacob and Matteson David S. (2017), "Sparse Identification and Estimation of High-Dimensional Vector AutoRegressive Moving Averages" arXiv preprint <arXiv:1707.09208>.

Examples

# Fit a sparse VAR model
data(Y)
VARfit <- sparseVAR(Y) # sparse VAR
Lhat <- lagmatrix(fit=VARfit, model="VAR") # get estimated lagmatrix
VARforecast <- directforecast(fit=VARfit, model="VAR", h=1) # get one-step ahead forecasts

[Package bigtime version 0.1.0 Index]