ComputeBest_tau: Run Monte Carlo simulation to investigate the optimal tau

In StableEstim: Estimate the Four Parameters of Stable Laws using Different Methods

Run Monte Carlo simulation to investigate the optimal τ

Description

Runs Monte Carlo simulation to investigate the optimal number of points to use when one of the reduced spacing schemes is considered.

Usage

ComputeBest_tau(AlphaBetaMatrix = abMat, nb_ts = seq(10, 100, 10),
                tScheme = c("uniformOpt", "ArithOpt"),
                Constrained = TRUE, alphaReg = 0.001, ...)

Arguments

AlphaBetaMatrix	values of the parameter α and β from which we simulate the data. By default, the values of γ and δ are set to 1 and 0, respectively; a 2 \times n matrix.
nb_ts	vector of number of t-points to use for the minimisation; default = seq(10,100,10).
tScheme	scheme used to select the points where the moment conditions are evaluated, one of "uniformOpt" (uniform optimal placement) and "ArithOpt" (arithmetic optimal placement). See function GMMParametersEstim.
Constrained	logical flag: if set to True, lower and upper bands will be computed as discussed for function GMMParametersEstim.
alphaReg	value of the regularisation parameter; numeric, default = 0.001.
...	Other arguments to pass to the optimisation function.

Value

a list containing slots from class Best_t-class corresponding to one value of the parameters α and β.

See Also

ComputeBest_t, Best_t-class

StableEstim documentation built on Aug. 7, 2022, 5:17 p.m.