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Abstract

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.

Keywords

st0713 posw high-dimensional model covariate selection partialing out stepwise Neyman-orthogonal generalized linear model postselection inference

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References

Belloni

Chen

Chernozhukov

Hansen

2012. Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica 80: 2369–2429. https://doi.org/10.3982/ECTA9626.

Belloni

Chernozhukov

2011. l₁-penalized quantile regression in high-dimensional sparse models. Annals of Statistics 39: 82–130. https://doi.org/10.1214/10-AOS827.

Belloni

Chernozhukov

Hansen

2014. Inference on treatment effects after selection among high-dimensional controls. Review of Economic Studies 81: 608–650. https://doi.org/10.1093/restud/rdt044.

Belloni

Chernozhukov

Wei

2016. Post-selection inference for generalized linear models with many controls. Journal of Business and Economic Statistics 34: 606–619. https://doi.org/10.1080/07350015.2016.1166116.

Chernozhukov

Hansen

Spindler

2015. Valid post-selection and postregularization inference: An elementary, general approach. Annual Review of Economics 7: 649–688. https://doi.org/10.1146/annurev-economics-012315-015826.

Drukker

D. M.

Liu

2022. Finite-sample results for lasso and stepwise Neymanorthogonal Poisson estimators. Econometric Reviews 41: 1047–1076. https://doi.org/10.1080/07474938.2022.2091363.

Drukker

D. M.

Liu

Forthcoming. A cluster plugin method for selecting the GLM lasso tuning parameters in models for unbalanced panel data. Econometrics and Statistics . https://doi.org/10.1016/j.ecosta.2022.02.006.

Kozbur

2020. Testing-based forward model selection. ArXiv Working Paper No. arXiv:1512.02666. https://doi.org/10.48550/arXiv.1512.02666.

Leeb

Pötscher

B. M.

2006. Can one estimate the conditional distribution of post-model-selection estimators? Annals of Statistics 34: 2554–2591. https://doi.org/10.1214/009053606000000821.

10.

Leeb

Pötscher

B. M.

2008. Sparse estimators and the oracle property, or the return of Hodges’ estimator. Journal of Econometrics 142: 201–211. https://doi.org/10.1016/j.jeconom.2007.05.017.

11.

Neyman

1959. Optimal asymptotic tests of composite statistical hypotheses. In Probability and Statistics: The Harald Cramér Volume, ed. Grenander

, 213–234. New York: Wiley.

12.

Neyman

1979. C(α) tests and their use. Sankhyaā 41: 1–21.

13.

Pötscher

B. M.

Leeb

2009. On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding. Journal of Multivariate Analysis 100: 2065–2082. https://doi.org/10.1016/j.jmva.2009.06.010.

14.

Sunyer

Suades-González

García-Esteban

Rivas

Pujol

Alvarez- Pedrerol

Forns

Querol

Basagaña

2017. Traffic-related air pollution and attention in primary school children: Short-term association. Epidemiology 28: 181–189. https://doi.org/10.1097/ede.0000000000000603.

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posw: A command for the stepwise Neyman-orthogonal estimator

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