This paper studies inference for multiple/many average partial effects when outcome variable is binary or fractional under a data rich, cluster sampling environment . 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 l1-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 and asymptotically non-conservative using a new multiplier cluster bootstrap  .
This paper studies inference for 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 .
D
a
R
environment
environments
R
l1-penalization
\ell_1-penalization
D
and asymptotically non-conservative
A
. We then apply algorithms to study multiple-testing problem of gendered language on the internet