fastVAR (1.2)


This package is designed for time series data. Uses fast implementations to estimate Vector Autoregressive models and Vector Autoregressive models with Exogenous Inputs. For speedup, fastVAR can use multiple cpu cores to calculate the estimates. For very large systems, fastVAR uses Lasso penalty to return very sparse coefficient matrices. Regression diagnostics can be used to compare models, and prediction functions can be used to calculate the n-step ahead prediction. Map-Reduce functions are in the works (Beta) for estimating large VAR models on a compute cluster.

Maintainer: Unknown
Author(s): Jeffrey Wong

License: GPL

Uses: glmnet, multicore

Released over 8 years ago.