rankSelectionCriterion: Sparse cointegration function used in determine_rank function...

View source: R/rank.R

rankSelectionCriterionR Documentation

Sparse cointegration function used in determine_rank function for Rank Selection Criterion

Description

Sparse cointegration function used in determine_rank function for Rank Selection Criterion

Usage

rankSelectionCriterion(
  p,
  Y,
  X,
  Z,
  r,
  alpha = NULL,
  beta,
  max.iter = 25,
  conv = 10^-3,
  lambda_gamma = 0.001,
  lambda_beta = matrix(seq(from = 2, to = 0.001, length = 100), nrow = 1),
  rho_omega = 0.5,
  cutoff = 0.8,
  intercept = F,
  exo = NULL,
  tol = 1e-04
)

Arguments

p

number of lagged differences

Y

Response Time Series

X

Time Series in Differences

Z

Time Series in Levels

r

cointegration rank

alpha

initial value for adjustment coefficients

beta

initial value for cointegrating vector

max.iter

maximum number of iterations

conv

convergence parameter

lambda_gamma

tuning paramter short-run effects

lambda_beta

tuning paramter cointegrating vector

rho_omega

tuning parameter inverse error covariance matrix

cutoff

cutoff value time series cross-validation approach

tol

tolerance parameter glmnet function

Value

A list containing: BETAhat: estimate of cointegrating vectors ALPHAhat: estimate of adjustment coefficients ZBETA: estimate of short-run effects OMEGA: estimate of inverse covariance matrix


jonlachmann/sparsecoint documentation built on April 14, 2022, 10:49 a.m.