Multivariate Factor Models

Multivariate factor models are statistical tools used to analyze multiple related variables simultaneously by identifying underlying factors that influence them. Think of them as a way to find common patterns or hidden drivers—like mood or economic conditions—that affect several variables, such as stock prices or test scores. By understanding these factors, we can better interpret complex data and predict future behavior. Essentially, they help reduce the complexity of large datasets by summarizing the key influences into fewer, manageable hidden factors.