ivlasso are routines for estimating structural parameters in linear models with many controls
and/or many instruments. The routines use methods for estimating sparse high-dimensional models, specifically the lasso
(Least Absolute Shrinkage and Selection Operator, Tibshirani 1996) and the square-root-lasso
(Belloni et al. 2011, 2014).
These estimators are used to select controls (
pdslasso) and/or instruments (
from a large set of variables (possibly numbering more than the number of observations), in a setting where the
researcher is interested in estimating the causal impact of one or more (possibly endogenous) causal variables
Two approaches are implemented in
- The post-double-selection methodology of Belloni et al. (2012, 2013, 2014, 2015, 2016).
- The post-regularization methodology of Chernozhukov, Hansen and Spindler (2015).
For instrumental variable estimation,
ivlasso implements weak-identification-robust hypothesis tests and confidence sets
using the Chernozhukov et al. (2013) sup-score test.
The implemention of these methods in
ivlasso require the Stata program
rlasso (available in the
separate Stata module lassopack), which provides lasso and square root-lasso estimation with data-driven penalization.