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

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.

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