bigtime {bigtime} | R Documentation |
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.
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.
Ines Wilms <ines.wilms@kuleuven.be>, Jacob Bien, David S. Matteson, Sumanta Basu
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>.
# 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