lph: Compute solution path for l_infinity or l1 constraints

In kolesarm/GMMSensitivity: Optimal Sensitivity Analysis in Generalized Method of Moments Models

Description

Computes the optimal sensitivity vector at each knot of the solution path that traces out the optimal bias-variance frontier when the set C takes the form c=B*gamma with the lp norm of gamma is bounded by a constant, for p=1, or p=Inf. This path is used as an input to OptEstimator.

Usage

lph(eo, B, p = Inf)

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 Initial 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 \mathcal{C}, where d_gamma is the number of invalid moments, and d_g is the number of moments
p	Parameter determining which lp norm to use, one of 1, or Inf.

Details

The algorithm is described in Appendix A of Armstrong and Kolesár (2020)

Value

Optimal sensitivity matrix. Each row corresponds optimal sensitivity vector at each step in the solution path.

References

Armstrong, T. B., and M. Kolesár (2020): Sensitivity Analysis Using Approximate Moment Condition Models, https://arxiv.org/abs/1808.07387v4

kolesarm/GMMSensitivity documentation built on Sept. 17, 2020, 5:47 p.m.