lpSetupInfeasible

List of IV-like estimates and the corresponding gamma
terms.

sset

This function separates the shape constraints from the LP
environment. That way, the model can be solved without any shape
constraints, which is the primary cause of infeasibility. This is
done in order to check which shape constraints are causing the
model to be infeasible. The LP model must be passed as an
environment variable, under the entry <code>$model</code>. See
<code><a rd-options="" href="/link/lpSetup?package=ivmte&version=1.4.0" data-mini-rdoc="ivmte::lpSetup">lpSetup</a></code>.

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 either linear programming
or quadratically constrained quadratic programming. Support for
four 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. MOSEK and the MOSEK R API can be
obtained from <https://www.mosek.com/>. The lp_solve library is freely
available from <http://lpsolve.sourceforge.net/5.5/>, and is included when
installing its API 'lpSolveAPI', which is available from CRAN.

Joshua Shea

ivmte

Instrumental Variables: Extrapolation by Marginal Treatment
Effects

lpSetupInfeasible function

This function separates the shape constraints from the LP
environment. That way, the model can be solved without any shape
constraints, which is the primary cause of infeasibility. This is
done in order to check which shape constraints are causing the
model to be infeasible. The LP model must be passed as an
environment variable, under the entry <code>$model</code>. See
<code><a rd-options='' href='lpSetup'>lpSetup</a></code>.

lpSetupInfeasible: Configure LP environment for diagnostics

Description

Usage

Arguments

Value