Automatically find the best vector autoregression models and networks for a given time series data set. 'AutovarCore' evaluates eight kinds of models: models with and without log transforming the data, lag 1 and lag 2 models, and models with and without day dummy variables. For each of these 8 model configurations, 'AutovarCore' evaluates all possible combinations for including outlier dummies (at 2.5x the standard deviation of the residuals) and retains the best model. Model evaluation includes the Eigenvalue stability test and a configurable set of residual tests. These eight models are further reduced to four models because 'AutovarCore' determines whether adding day dummies improves the model fit.

Documentation

Manual: autovarCore.pdf
Vignette: None available.

Maintainer: Ando Emerencia <ando.emerencia at gmail.com>

Author(s): Ando Emerencia*

Install package and any missing dependencies by running this line in your R console:

install.packages("autovarCore")

Depends
Imports Rcpp(>=0.11.4), Amelia, jsonlite, parallel, stats, urca, vars
Suggests testthat, roxygen2
Enhances
Linking to Rcpp
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Package autovarCore
Materials
URL
Task Views TimeSeries
Version 1.0-0
Published 2015-07-01
License MIT + file LICENSE
BugReports https://github.com/roqua/autovarcore/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks autovarCore check results
Package source autovarCore_1.0-0.tar.gz