ComputeBest_t {StableEstim}

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 \alpha and \beta 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 \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 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 \alpha and \beta.

See Also

ComputeBest_tau, Best_t-class