Factor models have become the most successful tool in the analysis and forecasting of high-dimensional time series. This monograph provides an extensive account of the so-called General Dynamic Factor Model methods. The topics covered include: asymptotic representation problems, estimation, forecasting, identification of the number of factors, identification of structural shocks, volatility analysis, and applications to macroeconomic and financial data.

This book argues that modern macroeconomics has completely overlooked the aggregate nature of the data. In Part I the homogeneity assumption is tested using disaggregate data and strongly rejected. As shown in Part II, the consequence of introducing heterogeneity is that, apart from flukes, cointegration unidirectional Granger causality, restrictions on parameters do not survive aggregation: thus the claim that modern macroeconomics has solid microfoundations is unwarranted.

However, it is argued in Part III that aggregation is not necessarily bad. Some important theory-based models that do not fit aggregate data well in their representative-agent version can be reconciled with aggregate data by introducing heterogeneity.