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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).
Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain
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.
Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming
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.
L1-Penalized Quantile Regression in High Dimensional Sparse Models
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.
High-Dimensional Methods and Inference on Structural and Treatment Effects
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.
Least Squares After Model Selection in High-Dimensional Sparse Models
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.
Some new asymptotic theory for least squares series: Pointwise and uniform results
Program evaluation and causal inference with high-dimensional data
- A. Belloni, V. Chernozhukov, Iv'an Fern'andez-Val, Christian Hansen
- Mathematics, Economics
- 11 November 2013
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.
ℓ[subscript 1]-penalized quantile regression in high-dimensional sparse models
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.…