nts.gamma.lassoreg: Function to estimate gamma, using l1 penalty ###

In jonlachmann/sparsecoint: Forecasting using sparse cointegration

Function to estimate gamma, using l1 penalty ###

Description

Function to estimate gamma, using l1 penalty ###

Usage

nts.gamma.lassoreg(
  Y,
  X,
  Z,
  alpha,
  beta,
  Omega,
  lambda = matrix(seq(from = 1, to = 0.01, length = 10), nrow = 1),
  p,
  cutoff = 0.8,
  intercept = F,
  exo = NULL,
  tol = 1e-04,
  fixed = FALSE
)

Arguments

Y	Response Time Series
X	Time Series in Differences
Z	Time Series in Levels
alpha	Estimate of the adjustment coefficients
beta	Estimate of the cointegrating vector
Omega	estimate of inverse error covariance matrix
lambda	tuning paramter short-run effects
p	number of lagged differences
cutoff	cutoff value time series cross-validation approach
intercept	F do not include intercept, T include intercept
tol	tolerance parameter glmnet function

Value

A list containing: gamma: estimate of short-run effects P: transformation matrix P derived from Omega

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