rankSelectionCriterion | R Documentation |
Sparse cointegration function used in determine_rank function for Rank Selection Criterion
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 )
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 |
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
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