OptEstimator: One-step estimator based on optimal sensitivity under lp...

Description Usage Arguments

Description

Computes the optimal sensitivity and the optimal estimator when the set C takes the form c=B*gamma with the lp norm of gamma bounded by M.

Usage

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OptEstimator(eo, B, M, p = 2, spath = NULL, alpha = 0.05,
  opt.criterion = "FLCI")

Arguments

eo

List containing initial estimates with the following components:

Sig

Estimate of variance of the moment condition, matrix with dimension d_g by d_g, where d_g is the number of moments

G

Estimate of derivative of the moment condition, matrix with dimension d_g by d_theta, where d_theta is the dimension of theta

H

Estimate of derivative of h(theta). A vector of length d_theta

n

sample size

h_init

Estimate of h(theta)

k_init

Initial sensitivity

g_init

Moment condition evaluated at initial estimate

B

matrix B with full rank and dimension d_g by d_gamma that determines the set C, where d_gamma is the number of invalid moments, and d_g is the number of moments

M

Bound on the norm of gamma

p

Parameter determining which lp norm to use, must equal 1, 2, or Inf.

spath

Optionally, the solution path, output of lph to speed up computation. For p==1 and p==Inf only.

alpha

determines confidence level, 1-alpha, for constructing/optimizing confidence intervals.

opt.criterion

Optimality criterion for choosing optimal bias-variance tradeoff. The options are:

"MSE"

Minimize worst-case mean squared error of the estimator.

"FLCI"

Length of (fixed-length) two-sided confidence intervals.

"Valid"

Optimal estimator under valid moments. This returns the original estimator, with confidence intervals adjusted for possible misspecification


kolesarm/GMMSensitivity documentation built on May 31, 2019, 1:51 a.m.