This paper studies inferencefor multiple/ many average partial effects when outcome variable is binary or fractional under data rich, cluster sampling environments. The number of average partial effects of interest can be much larger than number of sample clusters. We propose a post-double-selection estimator as well as a Neyman orthogonal moment estimator, both based on \ell_1-penalization, and explore their asymptotic properties. The proposed estimators do not require oracle property for valid inference . We propose easy-to-implement algorithms for  high-dimensional hypotheses testing and construction of simultaneously valid confidence intervals that are cluster-robust, robust against imperfect model selection using a new multiplier cluster bootstrap. We then apply algorithms to study multiple-testing problem of gendered language on the internet.
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.
R
This paper studies inferencefor multiple/
We study estimation, pointwise and simultaneous inference, and confidence intervals for
R
when outcome variable is binary or fractional under data rich, cluster sampling environments. The number of average partial effects of interest can be much larger than number of sample clusters. We propose a post-double-selection estimator as well as a Neyman orthogonal moment estimator, both based on \ell_1-penalization, and explore their
of lasso Logit. Focusing on high-dimensional, cluster-sampling environments, we propose a new average partial effect estimator and explore its
R
The proposed estimators do not require oracle property
Practical penalty choices compatible with our asymptotic theory are also provided. The proposed estimator allow
R
. We propose
without requiring oracle property. We provide
A
cluster-robust
R
hypotheses
hypothesis
R
that are cluster-robust, robust against imperfect model selection using a new
using a
R
then apply algorithms to study multiple-testing problem
apply the proposed algorithms to the text regression model of Wu (2018) to examine the presence