depCensoring (version 0.1.7)

lf.delta.beta1: Loss function to compute Delta(beta).

Description

This function defines the loss function used in computing the penalized local linear approximation of the test statistic in order to construct the bootstrap distribution of the test statistic.

Usage

lf.delta.beta1(
  Delta.sub,
  vnb,
  phi,
  Gn,
  Omegan,
  beta,
  c,
  r,
  data,
  par.space,
  epsilon.n,
  lambda.n
)

Value

Loss function evaluation evaluated at the given Delta.

Arguments

Delta.sub

Subvector of Delta.

vnb

Bootstrapped stochastic process.

phi

Moment selection functions.

Gn

First-order approximation matrix.

Omegan

Correlation matrix of sample moment functions.

beta

Coefficient vector.

c

Projection vector.

r

Value of projected coefficient vector.

data

Data frame.

par.space

Matrix containing the bounds on the parameter space.

epsilon.n

Parameter used in constructing the feasible region as in Example 4.1 in Bei (2024). Not used in this function.

lambda.n

Weight of penalty term.

References

Bei, X. (2024). Local linearieation based subvector inference in moment inequality models. Journal of Econometrics. 238:105549-