Dynamic Factor Models

Dynamic Factor Models are statistical tools used to analyze large sets of related data by identifying underlying common factors that drive observed patterns over time. Instead of examining each variable individually, they extract a few hidden factors that influence multiple variables simultaneously, capturing their shared dynamics. This approach simplifies complex data, helps forecast future trends, and reveals underlying relationships. Think of it like finding the main themes in a large collection of news articles—these themes (factors) explain much of the variation across all the articles. It's widely used in economics, finance, and other fields to interpret complex temporal data efficiently.