Time Series In High Dimensions: The General Dynamic Factor Model

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.

Marc Hallin is Professor Emeritus, Department of Mathematics, and Fellow, European Centre for Advanced Research in Economics and Statistics, Université libre de Bruxelles, Belgium. His research activity is in mathematical statistics, semiparametric and nonparametric inference, and time series. He is co-Editor-in-Chief of Statistical Inference for Stochastic Processes and an Associate Editor of the Journal of the American Statistical Association, the Journal of Econometrics, the Annals of Computational and Financial Econometrics, and the Journal of the Japan Statistical Society. A Fellow of the Institute of Mathematical Statistics (I.M.S.), of the American Statistical Association (A.S.A.), and of the International Statistical Institute (I.S.I.), he is member of the Classe des Sciences of the Royal Academy of Belgium. Marco Lippi is former Professor of Macroeconomics and Econometrics at the Università La Sapienza, and Fellow at the Einaudi Institute for Economics and Finance, Roma. His research interests are: Aggregation and the Foundations of Dynamic Macroeconomics, Fundamentalness and Identification in Macroeconomics, Persistence of Shocks, Representation and Estimation of Dynamic Factor Models, Factor Models and Cointegration. Matteo Barigozzi is Associate Professor in Statistics at the London School of Economics and Political Science (LSE), UK. His research mainly focuses on high-dimensional time series analysis and specifically on large dynamic factor models with extensions to the non-stationary setting, that is in presence of unit roots and cointegration or of change-points. He is interested also in applications to macroeconomic analysis, as monetary policy making, and financial analysis, as volatility forecasting. He is also working on sequential testing, models for network data and spectral analysis for modelling mixed frequencies data, non-linearities, and spatial dependencies. Mario Forni is Professor of Political Economy in the Faculty of Economics at the University of Modena and Reggio Emilia and Head of the Department of Political Economy. Research Fellow of the Center for Economic Policy Research (CEPR), London, he has been a consultant to the European Commission, the Banca d'Italia, and the Banque de France. His main research interests include Dynamic Macroeconomics, Time Series Analysis, Econometrics and Aggregation Theory. Paolo Zaffaroni is Professor in Financial Econometrics at the Business School, Imperial College, London. He is also teaching at the University of Rome La Sapienza, and has previously taught at the London School of Economics and at the University of Cambridge. His main research interests are financial econometrics and econometric theory as well as risk management and asset allocation. His work includes asset pricing and portolio choice with misspecified models, estimation of generalized dynamic factor models, estimation and testing of risk premia, term structure, multivariate volatility models, aggregation, and risk management.