loss_function: Compute loss function in a flexible way

In linogaliana/mindist: Minimum distance estimation in R

Description

Compute loss associated with a vector of structural parameters between some data and a transformation of structural parameters

Usage

loss_function(
  theta,
  prediction_function = {     function(theta) theta },
  weights = 1L,
  verbose = FALSE,
  return_moment = FALSE,
  moments_weights = NULL,
  moments_weighting_formula = "w ~ moments_weights",
  ...
)

Arguments

theta	Vector of structural parameters. Assuming a named vector.
prediction_function	Function that transforms θ into vector of moments. In Newey and MacFadden, denoted g(θ)
weights	Weight matrix W that should be used
verbose	Logical value indicating whether we want to print informative messages to monitor progress
return_moment	Logical value indicating whether we want to return moment matrix (observed, simulation, difference)
moments_weights	User-defined weights that should be applied to reweight moments importance. This is a user choice independent of using an optimal weight matrix
moments_weighting_formula	When relevent, how moment weighting should enter in the objective function
...	Additional arguments that should be used to control prediction_function behavior. This function should return a data.table object with a variable denoted epsilon giving the distance that should be minimized

Value

A distance between empirical and simulated moments

estimation_theta for GMM estimation

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