Many Average Partial Effects: with an Application to Text Regression

Harold Chiang

2019 Papers from Job Market Papers

Abstract: We study estimation, pointwise and simultaneous inference, and confidence intervals for many average partial effects of lasso Logit. Focusing on high-dimensional cluster-sampling environments, we propose a new average partial effect estimator and explore its asymptotic properties. Practical penalty choices compatible with our asymptotic theory are also provided. The proposed estimator allow for valid inference without requiring oracle property. We provide easy-to-implement algorithms for cluster-robust high-dimensional hypothesis testing and construction of simultaneously valid confidence intervals using a multiplier cluster bootstrap. We apply the proposed algorithms to the text regression model of Wu (2018) to examine the presence of gendered language on the internet.

JEL-codes: C23 C25 C55 (search for similar items in EconPapers)
Date: 2019-10-06
New Economics Papers: this item is included in nep-big and nep-ore
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Citations: View citations in EconPapers (2)

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Working Paper: Many Average Partial Effects: with An Application to Text Regression (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:jmp:jm2019:pch1836

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