rlassoIVselectX: Instrumental Variable Estimation with Selection on the...

In hdm: High-Dimensional Metrics

Instrumental Variable Estimation with Selection on the exogenous Variables by Lasso

Description

This function estimates the coefficient of an endogenous variable by employing Instrument Variables in a setting where the exogenous variables are high-dimensional and hence selection on the exogenous variables is required. The function returns an element of class rlassoIVselectX

Usage

rlassoIVselectX(x, ...)

## Default S3 method:
rlassoIVselectX(x, d, y, z, post = TRUE, ...)

## S3 method for class 'formula'
rlassoIVselectX(formula, data, post = TRUE, ...)

Arguments

x	exogenous variables in the structural equation (matrix)
...	arguments passed to the function rlasso
d	endogenous variables in the structural equation (vector or matrix)
y	outcome or dependent variable in the structural equation (vector or matrix)
z	set of potential instruments for the endogenous variables.
post	logical. If TRUE, post-lasso estimation is conducted.
formula	An object of class Formula of the form " y ~ x + d | x + z" with y the outcome variable, d endogenous variable, z instrumental variables, and x exogenous variables.
data	An optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which rlassoIVselectX is called.

Details

The implementation is a special case of of Chernozhukov et al. (2015). The option post=TRUE conducts post-lasso estimation for the Lasso estimations, i.e. a refit of the model with the selected variables. Exogenous variables x are automatically used as instruments and added to the instrument set z.

Value

An object of class rlassoIVselectX containing at least the following components:

coefficients	estimated parameter vector
vcov	variance-covariance matrix
residuals	residuals
samplesize	sample size

References

Chernozhukov, V., Hansen, C. and M. Spindler (2015). Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments American Economic Review, Papers and Proceedings 105(5), 486–490.

Examples

library(hdm)
data(AJR); y = AJR$GDP; d = AJR$Exprop; z = AJR$logMort
x = model.matrix(~ -1 + (Latitude + Latitude2 + Africa + 
                           Asia + Namer + Samer)^2, data=AJR)
dim(x)
  #AJR.Xselect = rlassoIV(x=x, d=d, y=y, z=z, select.X=TRUE, select.Z=FALSE)
  AJR.Xselect = rlassoIV(GDP ~ Exprop +  (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2 |
             logMort +  (Latitude + Latitude2 + Africa + Asia + Namer + Samer)^2,
             data=AJR, select.X=TRUE, select.Z=FALSE)
summary(AJR.Xselect)
confint(AJR.Xselect)

hdm documentation built on May 29, 2024, 3:35 a.m.