posw: A command for the stepwise Neyman-orthogonal estimator

• Autores: David M. Drukker, Di Liu
• Localización: The Stata journal, ISSN 1536-867X, Vol. 23, Nº. 2, 2023, págs. 402-417
• Idioma: inglés
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• Resumen

  • Inference for structural and treatment parameters while having high- dimensional covariates in the model is increasingly common. The Neyman-orthog- onal (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 covari- ates. Drukker and Liu (2022, Econometric Reviews 41: 1047–1076) extended the estimators in Belloni, Chernozhukov, and Wei (2016) by using a Bayesian infor- mation 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.