rlassoHAC: 'rlassoHAC' performs Lasso estimation under heteroscedastic...

In tsapp: Time Series, Analysis and Application

Description

rlassoHAC performs Lasso estimation under heteroscedastic and autocorrelated non-Gaussian disturbances.

Usage

rlassoHAC(
  x,
  y,
  kernel = "Bartlett",
  bands = 10,
  bns = 10,
  lns = NULL,
  nboot = 5000,
  post = TRUE,
  intercept = TRUE,
  model = TRUE,
  X.dependent.lambda = FALSE,
  c = 2,
  gamma = NULL,
  numIter = 15,
  tol = 10^-5,
  threshold = NULL,
  ...
)

Arguments

x	Regressors (vector, matrix or object can be coerced to matrix).
y	Dependent variable (vector, matrix or object can be coerced to matrix).
kernel	Kernel function, choose between "Truncated", "Bartlett" (by default), "Parzen", "Tukey-Hanning", "Quadratic Spectral".
bands	Bandwidth parameter with default bands=10.
bns	Block length with default bns=10.
lns	Number of blocks with default lns = floor(T/bns).
nboot	Number of bootstrap iterations with default nboot=5000.
post	Logical. If TRUE (default), post-Lasso estimation is conducted, i.e. a refit of the model with the selected variables.
intercept	Logical. If TRUE, intercept is included which is not penalized.
model	Logical. If TRUE (default), model matrix is returned.
X.dependent.lambda	Logical, TRUE, if the penalization parameter depends on the design of the matrix x. FALSE (default), if independent of the design matrix.
c	Constant for the penalty, default value is 2.
gamma	Constant for the penalty, default gamma=0.1/log(T) with T=data length.
numIter	Number of iterations for the algorithm for the estimation of the variance and data-driven penalty, ie. loadings.
tol	Constant tolerance for improvement of the estimated variances.
threshold	Constant applied to the final estimated lasso coefficients. Absolute values below the threshold are set to zero.
...	further parameters

Value

rlassoHAC returns an object of class "rlasso". An object of class "rlasso" is a list containing at least the following components:

coefficients	Parameter estimates.
beta	Parameter estimates (named vector of coefficients without intercept).
intercept	Value of the intercept.
index	Index of selected variables (logical vector).
lambda	Data-driven penalty term for each variable, product of lambda0 (the penalization parameter) and the loadings.
lambda0	Penalty term.
loadings	Penalty loadings, vector of lenght p (no. of regressors).
residuals	Residuals, response minus fitted values.
sigma	Root of the variance of the residuals.
iter	Number of iterations.
call	Function call.
options	Options.
model	Model matrix (if model = TRUE in function call).

Source

Victor Chernozhukov, Chris Hansen, Martin Spindler (2016). hdm: High-Dimensional Metrics, R Journal, 8(2), 185-199. URL https://journal.r-project.org/archive/2016/RJ-2016-040/index.html.

Examples

 

set.seed(1)
T = 100 #sample size
p = 20 # number of variables
b = 5 # number of variables with non-zero coefficients
beta0 = c(rep(10,b), rep(0,p-b))
rho = 0.1 #AR parameter
Cov = matrix(0,p,p)
for(i in 1:p){
  for(j in 1:p){
     Cov[i,j] = 0.5^(abs(i-j))
  }
} 
C <- chol(Cov)
X <- matrix(rnorm(T*p),T,p)%*%C
eps <- arima.sim(list(ar=rho), n = T+100)
eps <- eps[101:(T+100)] 
Y = X%*%beta0 + eps
reg.lasso.hac1 <- rlassoHAC(X, Y,"Bartlett") #lambda is chosen independent of regressor 
                                             #matrix X by default.

bn = 10 # block length
bwNeweyWest = 0.75*(T^(1/3))
reg.lasso.hac2 <- rlassoHAC(X, Y,"Bartlett", bands=bwNeweyWest, bns=bn, nboot=5000,
                            X.dependent.lambda = TRUE, c=2.7) 

tsapp documentation built on Oct. 30, 2021, 5:08 p.m.