Skip to search formSkip to main contentSkip to account menu
  • Publications
  • Influence
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).
View on SSRN
Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain
TLDR
A fully data-driven method for choosing the user-specified penalty that must be provided in obtaining LASSO and Post-LASSO estimates is provided and its asymptotic validity under non-Gaussian, heteroscedastic disturbances is established.
View on SSRN
Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming
TLDR
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 are significant, which achieves near-oracle performance, attaining the convergence rate σl(s/n) log pr-super-1/2 in the prediction norm.
View on SSRN
L1-Penalized Quantile Regression in High Dimensional Sparse Models
TLDR
This work proposes a pivotal, data-driven choice of the regularization parameter and shows that it satisfies certain theoretical constraints and evaluates the performance of L1-QR in a Monte-Carlo experiment, and provides an application to the analysis of the international economic growth.
View on SSRN
High-Dimensional Methods and Inference on Structural and Treatment Effects
TLDR
Using scanner datasets that record transaction-level data for households across a wide range of products, or text data where counts of words in documents may be wide range to text data, researchers are faced with a large set of potential variables formed by different ways of interacting and transforming the underlying variables.
View PDF
Least Squares After Model Selection in High-Dimensional Sparse Models
TLDR
Ols post lasso estimator can perform strictly better than lasso, in the sense of a strictly faster rate of convergence, if the lasso-based model selection correctly includes all components of the “true” model as a subset and also achieves sufficient sparsity.
View on SSRN
Some new asymptotic theory for least squares series: Pointwise and uniform results
View PDF on arXiv
Program evaluation and causal inference with high-dimensional data
TLDR
This paper shows that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters, and provides results on honest inference for (function-valued) parameters within this general framework where any high-quality, modern machine learning methods can be used to learn the nonparametric/high-dimensional components of the model.
View PDF on arXiv
ℓ[subscript 1]-penalized quantile regression in high-dimensional sparse models
View via Publisher
Optimizing Product Line Designs: Efficient Methods and Comparisons
We take advantage of recent advances in optimization methods and computer hardware to identify globally optimal solutions of product line design problems that are too large for complete enumeration.…
View PDF
...
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License