posw: A command for the stepwise Neyman-orthogonal estimator

David Drukker and Di Liu ()
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Di Liu: StataCorp

Stata Journal, 2023, vol. 23, issue 2, 402-417

Abstract: Inference for structural and treatment parameters while having highdimensional 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.

Keywords: posw; high-dimensional model; covariate selection; partialing out; stepwise; Neyman-orthogonal; generalized linear model; postselection inference (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1177/1536867X231175272

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