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posw: A command for the stepwise Neyman-orthogonal estimator

Author

Listed:
  • David M. Drukker

    (Sam Houston State University)

  • Di Liu

    (StataCorp)

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.

Suggested Citation

  • David M. Drukker & Di Liu, 2023. "posw: A command for the stepwise Neyman-orthogonal estimator," Stata Journal, StataCorp LP, vol. 23(2), pages 402-417, June.
    • Handle: RePEc:tsj:stataj:v:23:y:2023:i:2:p:402-417
    DOI: 10.1177/1536867X231175272
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