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Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain
We develop results for the use of LASSO and Post-LASSO methods to form first-stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p,Expand
Inference on Treatment Effects after Selection Amongst High-Dimensional Controls
TLDR
We propose robust methods for inference on the effect of a treatment variable on a scalar outcome in the presence of very many controls by conditioning on a relatively small number of controls whose identities are unknown. Expand
L1-Penalized Quantile Regression in High Dimensional Sparse Models
We consider median regression and, more generally, quantile regression in high-dimensional sparse models. In these models the overall number of regressors p is very large, possibly larger than theExpand
High-Dimensional Methods and Inference on Structural and Treatment Effects
TLDR
We consider estimation and inference in structural economic models allowing for very many conditioning variables or instruments. Expand
Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming
We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors areExpand
Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming
We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors areExpand
SUPPLEMENTARY APPENDIX FOR \INFERENCE ON TREATMENT EFFECTS AFTER SELECTION AMONGST HIGH-DIMENSIONAL CONTROLS"
In this supplementary appendix we provide additional results, omitted proofs and extensive simulations that complement the analysis of the main text (arXiv:1201.0224).
Least squares after model selection in high-dimensional sparse models
In this paper we study post-model selection estimators which apply ordinary least squares (ols) to the model selected by first-step penalized estimators, typically lasso. It is well known that lassoExpand
Some new asymptotic theory for least squares series: Pointwise and uniform results
In this work we consider series estimators for the conditional mean in light of three new ingredients: (i) sharp LLNs for matrices derived from the non-commutative Khinchin inequalities, (ii) boundsExpand
Program evaluation and causal inference with high-dimensional data
In this paper, we provide efficient estimators and honest con fidence bands for a variety of treatment eff ects including local average (LATE) and local quantile treatment eff ects (LQTE) inExpand
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