curvir (version 0.1.1)

varselect: Automatically select explanatory variables for a curve type

Description

Using cross-validation automatically select explanatory variables for a reserve demand curve type.

Usage

varselect(
  x,
  y,
  type = "logistic",
  folds = 10,
  constant = c(TRUE, FALSE),
  sign = NULL,
  reps = 3,
  search = c("backward", "forward"),
  wsel = c("select", "combine"),
  dummy = NULL
)

Value

Returns a list with the recommended variable selection choice:

• keep a logical vector with which variables to keep.

• errors statistics of the cross-validated MSE error.

Arguments

x

A matrix of explanatory variables. Excess reserve must be the first input.Additional regressor follow (optional).

y

A vector of normalised interest rates.

type

The type of the reserve demand curve. This can be any of logistic, redLogistic, fixLogistic, doubleExp, exponential, fixExponential, arctan, linear. See details in curve

folds

Folds to use for cross-validation.

constant

A logical (TRUE or FALSE) whether to include a constant or not.

sign

A vector of equal length to the number of additional regressors in x (excluding the constant (if used) and the excess reserves) of positive and negative values (any) that will be used to obtain signs to restrict the values of the estimated parameters. Use NULL for no restrictions.

reps

Number of repetitions for the particle swarm optimisation.

search

Search strategy for variable inclusion. With forward the model starts from no explanatory variables, and evaluates potential additions. With backward the model starts with all variables and evaluates for potential exclusions.

wsel

Use the minimum error set of parameters (select) or the combination of the pool parameters using the heuristic in Kourentzes et al., (2019) (combine).

dummy

Optional input to signify a regime change (vertical shifts in the curve). Must be a vector of equal length to the rows of x. If not needed use NULL.

Author

Nikolaos Kourentzes,

References

Chen, Z., Kourentzes, N., & Veyrune, R. (2023). Modeling the Reserve Demand to Facilitate Central Bank Operations. IMF Working Papers, 2023(179).

See Also

curve, and cvfit.

Examples

# \donttest{
  # Use ECB example data
  rate <- ecb$rate
  x <- ecb$x[,1:3,drop=FALSE]
  varKeep <- varselect(x,rate,folds=5)
  # Print result
  print(varKeep)
  # Fit curve with the selected variables
  curve(x[,varKeep$keep,drop=FALSE],rate)
# }