Online Program Home
My Program

Abstract Details

Activity Number: 274 - Macroeconomic Forecasting and Policy in Data Rich Digital Age Environments
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #300115
Title: Inference in High-Dimensional Models Without Regularization
Author(s): Ying Zhu* and Kaspar Wuthrich
Companies: UC San Diego and UC San Diego
Keywords: nonasymptotic analysis; high dimensional models; hypothesis testing; confidence regions; limited variability ; regularization free methods

Regularization methods have become the basis of many inference procedures for high-dimensional models where the number of covariates (p) is larger than or comparable to the sample size (n). I show an empirically relevant setting where regularization based inference methods exhibit substantial omitted variable biases and size distortions. Such a setting concerns limited variability in the covariates, which is common in applied economic research and there are many instances where it occurs “by design”. I then demonstrate situations where regularization-free methods can be adopted or developed for statistical inference in high dimensional regimes. For a new method proposed by Zhu (2019), I also discuss its connection with math programming under uncertainty. This talk is based on the two papers (available on ArXiv): 1. Omitted variable bias of Lasso-based inference methods under limited variability: A finite sample analysis. Kaspar Wüthrich, Ying Zhu (alphabetical ordering. Both authors contributed equally to this work) 2. Statistical inference and feasibility determination: a nonasymptotic approach. Ying Zhu

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program