rlassoHAC function

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

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.

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

Returns

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).

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)