LIPS_ind: 'Local' Integrated Propensity Score estimator based on...

In pedrohcgs/IPS: IPS: Covariate Distribution Balance via Integrated Propensity Scores

Description

'Local' Integrated Propensity Score estimator based on indicator weighting function

Usage

LIPS_ind(
  z,
  d,
  x,
  xbal = NULL,
  beta.initial = NULL,
  lin.rep = TRUE,
  whs = NULL,
  x_keep = FALSE,
  maxit = 50000
)

Arguments

z	An n x 1 vector of binary instruments.
d	An n x 1 vector of binary treatment adoption indicators.
x	An n x k matrix of covariates to be used in the propensity score. First element must be a vector of 1's.
xbal	An n x l, l≤q k, matrix of “raw” covariares to be balanced (does not need to include interaction terms). Default is NULL, which will use the same as x.
beta.initial	An optional k x 1 vector of initial values for the parameters to be optimized over.
lin.rep	Logical argument to whether an estimator for the asymptotic linear representation of the LIPS parameters should be provided. Deafault is TRUE.
whs	An optional n x 1 vector of weights to be used. If NULL, then every observation has the same weights.
x_keep	Default is FALSE. If TRUE, we return covariate matrix in the output.
maxit	The maximum number of iterations. Defaults to 50000. = FALSE). Deafault is 999 if boot = TRUE

Value

A list containing the following components:

coefficients	The estimated LIPS_ind coefficients
fitted.values	The LIPS_ind fitted probabilities
linear.predictors	The LIPS_ind estimated index (X'beta)
lin.rep	An estimator of the LIPS_ind coefficients' asymptotic linear representation
converged	An integer code. 0 indicates successful completion
x	The model matrix (i.e. the matrix of covariates used to estimate the LIPS_ind parameters). Only returned if x_keep = TRUE.

References

Sant'Anna, Pedro H. C, Song, Xiaojun, and Xu, Qi (2019), Covariate Distribution Balance via Propensity Scores, Working Paper <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3258551>.

pedrohcgs/IPS documentation built on Dec. 22, 2021, 7:39 a.m.