加载中…
个人资料
  • 博客等级:
  • 博客积分:
  • 博客访问:
  • 关注人气:
  • 获赠金笔:0支
  • 赠出金笔:0支
  • 荣誉徽章:
正文 字体大小:

Stata新命令-pdslasso:众多控制变量和工具变量如何挑选?

(2020-10-31 23:24:34)
标签:

pdslasso

stata

分类: Stata新命令
🍎 全文阅读:https://www.lianxh.cn/news/ccada6e4c41df.html

目录

 


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. 20112014).

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:

  1. The post-double-selection methodology of Belloni et al. (20122013201420152016).
  2. 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.

🍎 全文阅读:https://www.lianxh.cn/news/ccada6e4c41df.html

0

阅读 收藏 喜欢 打印举报/Report
  

新浪BLOG意见反馈留言板 欢迎批评指正

新浪简介 | About Sina | 广告服务 | 联系我们 | 招聘信息 | 网站律师 | SINA English | 产品答疑

新浪公司 版权所有