Advanced options for fgls

Tuning options of fgls() that most users never change. The defaults follow the papers and the Python package 'pyfgls'.

Usage

fgls_control(
  ic_penalty = c("order", "params"),
  min_lags = NULL,
  fold_scheme = c("random", "blocked"),
  foldid = NULL,
  cv_weights = c("fold", "full"),
  ridge_scale = c("standardized", "original"),
  n_lambda = 100L,
  lambda_min_ratio = NULL,
  lasso_engine = c("lars", "glmnet"),
  glmnet_thresh = 1e-07,
  delta_scale = FALSE,
  transforms = c("linear", "poly2", "log", "trig"),
  svr_cost = c(0.1, 1, 10, 100),
  svr_epsilon = c(0.05, 0.25, 0.5, 1),
  n_boot = 999L,
  bootstrap = c("wild", "iid"),
  wild_dist = c("rademacher", "mammen"),
  boot_seed = NULL,
  cores = 1L
)

Arguments

ic_penalty: Penalty of the lag-order criterion: "order" (default, PG26 eq. 14) penalizes the AR order; "params" penalizes every coefficient a lag adds to the Durbin regression, which avoids over-selection with many stochastic regressors.
min_lags: Lower bound on the selected AR order: NULL (default), a non-negative integer, or "adjusted" for \(\lceil(\ln T)^{3/2}\rceil\) (PG26 Supplement, Lemma A.2).
fold_scheme: Cross-validation folds of the skedastic fit: "random" (default) or "blocked" (contiguous blocks).
foldid: Optional integer vector of fold labels (one per observation entering the skedastic fit), overriding fold_scheme and the seed.
cv_weights: "fold" (default) re-estimates the ridge first step, and hence the adaptive weights, on every training fold; "full" computes the weights once on the full sample (the common practice with glmnet::cv.glmnet() and penalty.factor).
ridge_scale: Scale on which the ridge coefficients define the adaptive weights: "standardized" (default) or "original" units of z.
n_lambda: Length of the Lasso penalty grid.
lambda_min_ratio: Smallest penalty relative to the largest; default 1e-4 when there are more observations than candidate covariates and 1e-2 otherwise (as in 'glmnet').
lasso_engine: Solver of the Lasso problems: "lars" (default) computes the exact solution path by the LARS homotopy with the Lasso modification, as 'pyfgls' does; "glmnet" uses glmnet::glmnet() (coordinate descent) followed by an exact active-set refinement. Both give the same solutions; "lars" is faster for the moderate numbers of candidate covariates typical here.
glmnet_thresh: Convergence threshold passed to glmnet::glmnet() when lasso_engine = "glmnet".
delta_scale: Use the scale-aware floor delta * sigma_OLS in \(\log\max(\hat u_t^2, \delta^2)\).
transforms: Transform families used by z = "auto"; see make_dictionary().
svr_cost, svr_epsilon: Grids for the SVR skedastic fit, multiplied by the standard deviation of the response.
n_boot: Number of bootstrap replications for vcov = "bootstrap".
bootstrap: Innovation scheme of the sieve bootstrap: "wild" (default) or "iid" resampling.
wild_dist: Wild-bootstrap multipliers: "rademacher" (default) or "mammen".
boot_seed: Seed of the bootstrap; default the seed of the fit.
cores: Number of cores for the bootstrap (forked processes via 'parallel'; ignored on Windows). Defaults to 1.

Value

A list of class "fgls_control".

Examples

fgls_control(ic_penalty = "params", n_boot = 199)
#> $ic_penalty
#> [1] "params"
#> 
#> $min_lags
#> NULL
#> 
#> $fold_scheme
#> [1] "random"
#> 
#> $foldid
#> NULL
#> 
#> $cv_weights
#> [1] "fold"
#> 
#> $ridge_scale
#> [1] "standardized"
#> 
#> $n_lambda
#> [1] 100
#> 
#> $lambda_min_ratio
#> NULL
#> 
#> $lasso_engine
#> [1] "lars"
#> 
#> $glmnet_thresh
#> [1] 1e-07
#> 
#> $delta_scale
#> [1] FALSE
#> 
#> $transforms
#> [1] "linear" "poly2"  "log"    "trig"  
#> 
#> $svr_cost
#> [1]   0.1   1.0  10.0 100.0
#> 
#> $svr_epsilon
#> [1] 0.05 0.25 0.50 1.00
#> 
#> $n_boot
#> [1] 199
#> 
#> $bootstrap
#> [1] "wild"
#> 
#> $wild_dist
#> [1] "rademacher"
#> 
#> $boot_seed
#> NULL
#> 
#> $cores
#> [1] 1
#> 
#> attr(,"class")
#> [1] "fgls_control"

Usage

Arguments

Value

See also

Examples