High-Dimensional Regression and Factor Models

When we have more potential predictors than we can comfortably count, ordinary regression breaks down — either the numbers don’t add up or the estimates become wildly unstable. This chapter gives you a set of practical tools for handling high‑dimensional data. We use shrinkage methods to build stable predictive models, principal components to compress many variables into a few meaningful summaries, and factor models to find the hidden forces driving dozens or hundreds of series at once.