curveopt {curvir}

Optimise curve parameters

Description

Finds optimal curve parameters.

Usage

curveopt(
  x,
  y,
  type = "logistic",
  constant = c(TRUE, FALSE),
  reps = 3,
  sign = NULL,
  q = NULL,
  winit = NULL,
  yhat = NULL,
  wsel = c("select", "combine"),
  dummy = NULL,
  sameSign = c(TRUE, FALSE)
)

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

constant

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

reps

Number of repetitions for the particle swarm optimisation.

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.

q

The desired quantile to optimise for. Use NULL to get the conditional expectation.

winit

A vector of initial values for the optimisation. This will also carry over to sign restrictions if sameSign==TRUE.

yhat

Useful when estimating quantiles. Supply here the predicted values for the conditional expectation to add restrictions for the quantiles to not cross the conditional expectation. Use NULL to not add any restrictions.

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.

sameSign

Used if winit != NULL to take any sign restrictions from winit.

Value

Returns a list of

• w The optimal parameters

• mse The Mean Squared Error of the fitted curve.

Author(s)

Nikolaos Kourentzes, [e-mail]

References

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

• Kourentzes, N., Barrow, D., & Petropoulos, F. (2019). Another look at forecast selection and combination: Evidence from forecast pooling. International Journal of Production Economics, 209, 226-235.

See Also

curve, and curvepred.

Examples

  # Use ECB example data
  rate <- ecb$rate
  x <- ecb$x[,1,drop=FALSE]
  curveopt(x,rate)