JGrLasso: JGrLasso

In lucabarbaglia/t-VAR: Penalized Vector AutoRegressions with t-innovations

Description

Function for the joint estimation of of beta and omega

Usage

JGrLasso(
  Y,
  X,
  P,
  n,
  J,
  type_lasso = "Lasso",
  maxiter.both = 50,
  tol.both = 0.01,
  lambda1.min = NULL,
  lambda1.max = NULL,
  lambda1.steps = NULL,
  gamma1.min = NULL,
  gamma1.max = NULL,
  gamma1.steps = NULL,
  lambda1.OPT = NULL,
  gamma1.OPT = NULL
)

Arguments

Y	a vector of length J(N-P) cointaining the responses. Obtained with funxtion "stack_y". J: number of time series. N: time series length.
X	a J(N-P)xPJ^2 matrix cointainig the regressor. Obtained with function "stack_Xbig".
P	VAR order.
type_lasso	type of lasso penalty. "Lasso" for standard lasso, "Group" for group lasso. Default is "Lasso".
tol.both	tolerance gaussian lasso algorithm. Default is 0.01.
maxit.both	maximum iterations for gaussian lasso algorithm. Default is 50.
lambda1_min	minimum value of the regularization parameter on Beta. Default is NULL.
lambda1_max	maximum value of the regularization parameter on Beta. Default is NULL.
lambda1_steps	number of steps in the lambda grid. Default is NULL.
gamma1_min	minimum value of the regularization parameter on Omega. Default is NULL.
gamma1_max	maximum value of the regularization parameter on Omega. Default is NULL.
gamma1_steps	number of steps in the gamma grid. Default is NULL.
lambda1_OPT	optimal value of the regularization parameter on Beta. Default is NULL.
gamma1_OPT	optimal value of the regularization parameter on Omega. Default is NULL.

Value

A list containing two objects:

"beta.new"	a vector containing the estimated Beta.
"beta.arr"	a JxJxP array containing the estimated Beta.
"omega.new"	a JxJ matrix containing the estimated Omega.
"Obj_JGrL"	objective function.
"iter"	number of iterations.
"lambda"	selected value of the regularization parameter on Beta.
"gamma"	selected value of the regularization parameter on Omega.

Author(s)

Luca Barbaglia https://lucabarbaglia.github.io/

References

Barbaglia, L., Croux, C., & Wilms, I. (2020). Volatility spillovers in commodity markets: A large t-vector autoregressive approach. Energy Economics, 85, 104555.

lucabarbaglia/t-VAR documentation built on Feb. 27, 2021, 3:46 a.m.