specs_tr_opt: SPECS with data transformation and penalty optimization
In specs: Single-Equation Penalized Error-Correction Selector (SPECS)

Description Usage Arguments Value Examples

View source: R/specs.R

The same function as specs_tr(...), but on data that is pre-transformed to a CECM.

specs_tr_opt(
  y_d,
  z_l = NULL,
  w,
  rule = c("BIC", "AIC", "TSCV"),
  CV_cutoff = 2/3,
  deterministics = c("constant", "trend", "both", "none"),
  ADL = FALSE,
  weights = c("ridge", "ols", "none"),
  k_delta = 1,
  k_pi = 1,
  lambda_g = NULL,
  lambda_i = NULL,
  thresh = 1e-04,
  max_iter_delta = 1e+05,
  max_iter_pi = 1e+05,
  max_iter_gamma = 1e+05
)

`y_d`	A vector containing the differences of the dependent variable.
`z_l`	A matrix containing the lagged levels.
`w`	A matrix containing the required difference.
`rule`	A charcater object indicating which selection rule the optimal choice of the penalty parameters is based on. Default is "BIC".
`CV_cutoff`	A numeric value between 0 and 1 that decides the proportion of the training sample as a fraction of the complete sample. Applies only when rule="TSCV". Default is 2/3.
`deterministics`	A character object indicating which deterministic variables should be added ("none","constant","trend","both"). Default is "constant".
`ADL`	Logical object indicating whether an ADL model without error-correction term should be estimated. Default is FALSE.
`weights`	Choice of penalty weights. The weights can be automatically generated by ridge regression (default) or ols. Alternatively, a conformable vector of non-negative weights can be supplied.
`k_delta`	The power to which the weights for delta should be raised, if weights are set to "ridge" or "ols".
`k_pi`	The power to which the weights for pi should be raised, if weights are set to "ridge" or "ols".
`lambda_g`	An optional user-specified grid for the group penalty may be supplied. If left empty, a 10-dimensional grid containing 0 as the minimum value is generated.
`lambda_i`	An optional user-specified grid for the individual penalty may be supplied. If left empty, a 10-dimensional grid containing 0 as the minimum value is generated.
`thresh`	The treshold for convergence.
`max_iter_delta`	Maximum number of updates for delta. Default is 10^5.
`max_iter_pi`	Maximum number of updates for pi. Default is 10^5.
`max_iter_gamma`	Maximum number of updates for gamma. Default is 10^5.

`D`	A matrix containing the deterministic variables included in the model.
`gammas`	A matrix containing the estimated coefficients of the stochastic variables in the conditional error-correction model.
`gamma_opt`	A vector containing the estimated coefficients corresponding to the optimal model.
`lambda_g`	The grid of group penalties.
`lambda_i`	The grid of individual penalties.
`theta`	The estimated coefficients for the constant and trend. If a deterministic component is excluded, its coefficient is set to zero.
`theta_opt`	The estimated coefficients for the constant and trend in the optimal model.
`v`	A matrix containing the independent variables (excluding deterministic components).
`weights`	The vector of penalty weights.
`y_d`	A vector containing the dependent variable, i.e. the differences of y.

#Estimate a CECM with a constant, ols initial weights and penalty chosen by the minimum AIC

#Organize data
y <- Unempl_GT[,1]
index_GT <- sample(c(2:ncol(Unempl_GT)),10)
x <- Unempl_GT[,index_GT]
y_d <- y[-1]-y[-100]
z_l <- cbind(y[-100],x[-100,])
w <- x[-1,]-x[-100,] #This w corresponds to a cecm with p=0 lagged differences

my_specs <- specs_tr_opt(y_d,z_l,w,rule="AIC",weights="ols",deterministics="constant")