approx_jacobian_epsilon: Approximate jacobian matrix for loss function

In linogaliana/mindist: Minimum distance estimation in R

Description

A numerical estimation for the jacobian matrix giving the change of loss function dimensions \mathcal{l}(θ) for marginal change in each dimension of θ vector

Usage

approx_jacobian_epsilon(theta, model_function, step = 1e-06, ...)

Arguments

theta	Vector of structural parameters. Assuming a named vector.
model_function	Function that should be used to transform θ parameter into moment conditions
step	h step to numerically compute derivative
...	Additional arguments

Details

Jacobian matrix is not derived from gradient methods but is numerically approximated using a small h step (step argument).

Parallel implementation is proposed but is not efficient for the moment: it is usually slower than the sequential approach

linogaliana/mindist documentation built on July 11, 2021, 4:22 a.m.