ComputeBest_t: Monte Carlo simulation to investigate the optimal number of...

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

Monte Carlo simulation to investigate the optimal number of points to use in the moment conditions

Description

Runs Monte Carlo simulation for different values of α and β and computes a specified number of t-points that minimises the determinant of the asymptotic covariance matrix.

Usage

ComputeBest_t(AlphaBetaMatrix = abMat, nb_ts = seq(10, 100, 10),
              alphaReg = 0.001, FastOptim = TRUE, ...)

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 numbers of t-points to use for the minimisation; default = seq(10, 100, 10).
alphaReg	value of the regularisation parameter; numeric, default = 0.001.
FastOptim	Logical flag; if set to TRUE, optim with "Nelder-Mead" method is used (fast but not accurate). Otherwise, nlminb is used (more accurate but slower).
...	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_tau, Best_t-class

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