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View Code? Open in Web Editor NEWR package for Vector Autoregression
R package for Vector Autoregression
The native lm call on a response matrix just makes multiple calls to lm on a univariate response vector. VAR models can be computed more efficiently by solving the normal equations in parallel
R >= 2.14 has a built in parallel library which inherits from snow and multicore. This will allow parallel computation of glmnet on both windows and linux
This means SparseVAR and SparseVARX will not be able to take intercept=F
first few values are correct but next few values are NA
allows for multi response lasso regression
The weighted least squares problem has the normal equations
(X^T W X)^-1 X^T W Y
use GNU scientific library to implement faster versions of VAR and VARX
Create a helper function to autogenerate the specification matrix for SparseVAR and SparseVARX. These matrices are used to determine which endogeneous variables are regressed on which other endogenous variables. This can be determined by clustering similar time series together
Preprocess data by removing seasonality and trend. See Rob Hyndman's decompose function in package forecast
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