Run Monte Carlo simulation to investigate the optimal \(\tau\)

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 \(\alpha\) and \(\beta\) from which we simulate the data. By default, the values of \(\gamma\) and \(\delta\) 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 \(\alpha\) and \(\beta\).

See also

ComputeBest_t, Best_t-class