Stata新命令-pdslasso：众多控制变量和工具变量如何挑选？
2020-10-31 23:24阅读：
Stata package:
pdslasso
pdslasso and
ivlasso are routines
for estimating structural parameters in linear models with many
controls and/or 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.
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