the matrix of covariates (includes endogenous and
exogenous covariates).

the matrix of instruments (includes exogenous covariates
in the second stage).

vector of variable names from the second stage
that we want to include in the S-set of IV-like estimands.

lmcomponents

weights

This function performs TSLS to obtain the estimates for the IV-like
estimands.

The marginal treatment effect was introduced by Heckman and
Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a
choice-theoretic interpretation to instrumental variables models that
maintain the monotonicity condition of Imbens and Angrist (1994)
<doi:10.2307/2951620>. This interpretation can be used to extrapolate from
the compliers to estimate treatment effects for other subpopulations. This
package provides a flexible set of methods for conducting this
extrapolation. It allows for parametric or nonparametric sieve estimation,
and allows the user to maintain shape restrictions such as monotonicity. The
package operates in the general framework developed by Mogstad, Santos and
Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point
identification or partial identification (bounds). In the partially
identified case, bounds are computed using linear programming. Support for
three linear programming solvers is provided. Gurobi and the Gurobi R API
can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained
from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex'
and 'cplexAPI' are available from CRAN. The lp_solve library is freely
available from <http://lpsolve.sourceforge.net/5.5/>, and is included when
installing either of its R APIs, 'lpSolve' or 'lpSolveAPI', which are
available from CRAN.

piv: Obtaining IV-like estimands

Description

Usage

Arguments

Value