ElasticNetVAR: Elastic Net vector autoregression

In YiffyGuo/GabauerDavid-ConnectednessApproach: Connectedness Approach

Elastic Net vector autoregression

Description

Estimation of a VAR using equation-by-equation LASSO, Ridge or Elastic Net regressions.

Usage

ElasticNetVAR(
  x,
  configuration = list(nlag = 1, nfolds = 10, loss = "mae", alpha = NULL, delta_alpha =
    0.1)
)

Arguments

x	zoo data matrix
configuration	Model configuration
nlag	Lag length
nfolds	N-fold cross validation
loss	Loss function
alpha	LASSO is alpha equal 1 and Ridge if alpha equal 0
delta_alpha	Steps between 0 and 1. If alpha is NULL alpha is estimated based upon loss and nfolds

Value

Estimate VAR model

Author(s)

David Gabauer

References

Tibshirani, R., Bien, J., Friedman, J., Hastie, T., Simon, N., Taylor, J., & Tibshirani, R. J. (2012). Strong rules for discarding predictors in lasso‐type problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74(2), 245-266.\ Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.\ Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology), 67(2), 301-320.\ Demirer, M., Diebold, F. X., Liu, L., & Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33(1), 1-15.\ Gabauer, D., Gupta, R., Marfatia, H., & Miller, S. M. (2020). Estimating US Housing Price Network Connectedness: Evidence from Dynamic Elastic Net, Lasso, and Ridge Vector Autoregressive Models. Lasso, and Ridge Vector Autoregressive Models (July 26, 2020).

Examples

data(dy2012)
fit = ElasticNetVAR(dy2012, configuration=list(nlag=1, alpha=1, nfolds=10, loss="mae"))

YiffyGuo/GabauerDavid-ConnectednessApproach documentation built on April 15, 2022, 5:20 p.m.