Debiased Inference of Average Partial Effects in Single-Index Models
By David A. Hirshberg, Stefan Wager. 2018
15 citations recorded by the source corpus.
We propose a method for average partial effect estimation in high-dimensional single-index models that is root-n-consistent and asymptotically unbiased given sparsity assumptions on the underlying regression model. This note was prepared as a comment on Wooldridge and Zhu [2018], forthcoming in the Journal of Business and Economic Statistics.
Subfields: Economics and Econometrics, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Stefan Wager
- Victor Chernozhukov
- Whitney K. Newey
- Rahul Singh
- David A. Hirshberg
- Christian Hansen
- Iv'an Fern'andez-Val
- Guido W. Imbens
In Statistics and Probability
- Stefan Wager
- David A. Hirshberg
- Victor Chernozhukov
- Rahul Singh
- Whitney K. Newey
- Susan Athey
- Guido W. Imbens
- Christian Hansen
Citations in the corpus
- Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, background): “Hirshberg and Wager (2018) consider the average derivative functional in a single index model, analyzing a variant of the estimator proposed here, adapted to the single-index regression structure.”
- Augmented minimax linear estimation (2017, background): “While it is not necessary to estimate m̂ on an auxilliary sample when estimating linear functionals, we do this in the nonlinear case discussed in a forthcoming commentary (Hirshberg and Wager, 2018).”