# Stata package: pdslasso

`pdslasso`

and `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 (`ivlasso`

)
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
of interest.

Two approaches are implemented in `pdslasso`

and `ivlasso`

:

- 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 `pdslasso`

and `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.