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Many Average Partial Effects: with An Application to Text Regression

Harold D. Chiang

arXiv 21 Dec 2018 · Econometrics · 3 citations (OpenAlex)

arXiv:1812.09397 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

43
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105
in-text mentions
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main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Wu, A. H (2018) Gendered language on the economics job market rumors forum, in1.000156100%
2Wooldridge, J. and Y. Zhu (2017) Inference in approximately sparse correlated random effects probit models0.81142100%
3Belloni, A., V. Chernozhukov, and K. Kato (2015) Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems0.7375340%
4Belloni, A., V. Chernozhukov, and Y. Wei (2016) b): Post-selection inference for generalized linear models with many controls0.7374350%
5Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen (2012) Sparse models and methods for optimal instruments with an application to eminent domain0.7373367%
6Kock, A. B (2016) Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models0.7373367%
7Hirshberg, D. A. and S. Wager (2018) Debiased inference of average partial effects in single-index models0.73732100%
8Belloni, A., V. Chernozhukov, D. Chetverikov, and Y. Wei (2018) Uniformly valid post-regularization confidence regions for many functional parameters in Z-estimation framework0.66517829%
9Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls0.64422100%
10Belloni, A., V. Chernozhukov, C. Hansen, and D. Kozbur (2016) a): Inference in high-dimensional panel models with an application to gun control0.64422100%

Showing the top 10 of 43 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models0.40511
2Should Humans Lie to Machines? The Incentive Compatibility of Lasso and General Weighted Lasso0.40511
3Tuning Parameter Selection in Econometrics0.40511