Inference for High-Dimensional Sparse Econometric Models

Understand

This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics.

• High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is well-approximated by a parsimonious, yet unknown set of regressors.
• The latter condition makes it possible to estimate the entire regression function effectively by searching for approximately the right set of regressors.
• We discuss methods for identifying this set of regressors and estimating their coefficients based on $\ell_1$-penalization and describe key theoretical results.