SparseCointegration_Lasso: Main function to perform sparse cointegration

In jonlachmann/sparsecoint: Forecasting using sparse cointegration

Main function to perform sparse cointegration

Description

Main function to perform sparse cointegration

Usage

SparseCointegration_Lasso(
  data,
  p,
  r,
  alpha = NULL,
  beta = NULL,
  max.iter = 10,
  conv = 10^-2,
  lambda_gamma,
  rho_omega,
  lambda_beta,
  cutoff = 0.8,
  tol = 1e-04,
  intercept = FALSE
)

Arguments

p	number of lagged differences
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
rho_omega	tuning parameter inverse error covariance matrix
lambda_beta	tuning paramter cointegrating vector
cutoff	cutoff value time series cross-validation approach
tol	tolerance parameter glmnet function
Y	Response Time Series
X	Time Series in Differences
Z	Time Series in Levels

Value

A list containing: beta: estimate of cointegrating vectors alpha: estimate of adjustment coefficients gamma: estimate of short-run effects omega: estimate of inverse covariance matrix

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