sTslsSplines

vector, the value of the instrument.

0 or 1, indicating treatment or control (redundant in this
function; included to exploit apply()).

scalar, position of the component one is interested in
constructing the IV-like weight for.

matrix, corresponds to E[XZ']E[ZZ']^-1, the first stage
regression.

IV-like weighting function for TSLS specification.

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.

sTslsSplines: IV-like weighting function, TSLS specification

Description

Usage

Arguments

Value