posw：阶跃Neyman正交估计器的一个命令

IF 3.2 2区数学 Q1 SOCIAL SCIENCES, MATHEMATICAL METHODS

Stata Journal Pub Date : 2023-06-01 DOI:10.1177/1536867X231175272

D. Drukker, Di Liu

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引用次数: 0

摘要

在模型中具有高维协变量的情况下，对结构和处理参数的推断越来越普遍。Belloni, Chernozhukov, and Wei (2016, Journal of Business and Economic Statistics 34: 606-619)的Neyman-orthogonal (NO)估计在使用广义线性模型lasso方法选择协变量的同时，对感兴趣的参数产生有效的推断。Drukker和Liu (2022, Econometric Reviews 41: 1047-1076)采用贝叶斯信息准则逐步方法和检验-逐步方法作为协变量选择器，扩展了Belloni、Chernozhukov和Wei(2016)的估计量。Drukker和Liu(2022)发现了一系列数据生成过程，其中基于贝叶斯信息准则的NO估计器逐步产生比基于laso的NO估计器更可靠的推断。在本文中，我们描述了posw的实现，posw是用于高维线性、logit和泊松模型的基于逐步的NO估计器的命令。

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posw: A command for the stepwise Neyman-orthogonal estimator

Inference for structural and treatment parameters while having high-dimensional covariates in the model is increasingly common. The Neyman-orthogonal (NO) estimators in Belloni, Chernozhukov, and Wei (2016, Journal of Business and Economic Statistics 34: 606–619) produce valid inferences for the parameters of interest while using generalized linear model lasso methods to select the covariates. Drukker and Liu (2022, Econometric Reviews 41: 1047–1076) extended the estimators in Belloni, Chernozhukov, and Wei (2016) by using a Bayesian information criterion stepwise method and a testing-stepwise method as the covariate selector. Drukker and Liu (2022) found a family of data-generating processes for which the NO estimator based on Bayesian information criterion stepwise produces much more reliable inferences than the lasso-based NO estimator. In this article, we describe the implementation of posw, a command for the stepwise-based NO estimator for the high-dimensional linear, logit, and Poisson models.

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来源期刊

Stata Journal 数学-统计学与概率论

CiteScore

7.80

自引率

4.20%

发文量

审稿时长

>12 weeks

期刊介绍： The Stata Journal is a quarterly publication containing articles about statistics, data analysis, teaching methods, and effective use of Stata''s language. The Stata Journal publishes reviewed papers together with shorter notes and comments, regular columns, book reviews, and other material of interest to researchers applying statistics in a variety of disciplines.