Forecasting Non-Stationary Economic Time Series
In their second book on economic forecasting, Michael Clements and David Hendry ask why some practices seem to work empirically despite a lack of formal support from theory. After reviewing the conventional approach to economic forecasting, they look at the implications for causal modeling, present a taxonomy of forecast errors, and delineate the sources of forecast failure. They show that forecast-period shifts in deterministic factors—interacting with model misspecification, collinearity, and inconsistent estimation—are the dominant source of systematic failure. They then consider various approaches for avoiding systematic forecasting errors, including intercept corrections, differencing, co-breaking, and modeling regime shifts; they emphasize the distinction between equilibrium correction (based on cointegration) and error correction (automatically offsetting past errors). Their results on forecasting have wider implications for the conduct of empirical econometric research, model formulation, the testing of economic hypotheses, and model-based policy analyses.
About the Authors
Michael P. Clements is Research Fellow in Economics at the Universityof Warwick, UK.
David F. Hendry is Professor of Economics and Director of the Program in Economic Modeling, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
“Clements and Hendry have produced a fascinating and insightful book, one that makes us think hard about what we do, why we do it, and how we could do it better. And perhaps unusually for economists, they provide some answers, using serious scholarship to produce useful, practical recommendations. Bravo!”
—Francis X. Diebold, Visiting Professor of Finance, Stern School of Business, New York University, and Lawrence R. Klein Professor of Economics, University of Pennsylvania
“Forecasters are well aware of the difficulties of forecasting in economies in which some structural changes are occurring, and have evolved many ad-hoc methods to compensate for such changes. Despite this, surprisingly little analysis has appeared investigating either types of structural change that would be most deleterious to forecasting accuracy or which of the ad-hoc methods would be best to use when faced with such changes. This book provides a formal treatment of these issues and will become a landmark in research on forecasting methods in such environments. It provides an excellent balance of formal analysis and intuition. Anyone interested in issues of forecasting would benefit greatly from studying it.”
—Adrian Pagan, Economics Program, Australian National University
“This is an important and provocative contribution to the theory and methodology of economic forecasting. It will be essential reading for anyone with a serious professional interest in the field.”
—Paul Newbold, Professor of Econometrics, University of Nottingham