Forecasting Economic Time Series

An extended formal analysis of economic forecasting co-authored by one of the world's leading econometricians.

Michael Clements (Author), David Hendry (Author)

9780521634809, Cambridge University Press

Paperback, published 8 October 1998

392 pages, 43 tables
22.9 x 15.2 x 2.2 cm, 0.57 kg

'Perhaps one of the most appealing features of the book is the systematic way in which it outlines and uncovers problems in forecasting, lays out possible solutions, and uses Monte Carlo, theoretical and empirical evidence to assess the potential solutions. Another appealing feature is that beginning researchers who are generally interested in serious (empirical) scientific investigation can learn much from noting how Clements and Hendry uncover, assess, and examine important issues in the area of economic forecasting. A third feature worth noting is the plethora of insightful and detailed empirical and Monte Carlo evidence. Forecasting Economic Time Series not only elucidates in detailed fashion how to construct macroeconomic forecasts, but also contains many hints on how to construct good macroeconomic forecasts. This makes it a must for forecasters' Journal of the American Statistical Association

This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.

1. An introduction to economic forecasting
2. First principles
3. Evaluating forecast accuracy
4. Forecasting in univariate processes
5. Monte Carlo techniques
6. Forecasting in co-intergrated systems
7. Forecasting with large-scale macro-econometric models
8. A theory of intercept corrections: beyond mechanistic forecasts
9. Forecasting using leading indicators
10. Combining forecasts
11. Multi-step estimation
12. Parsimony
13. Testing forecast accuracy
14. Postscript.

Subject Areas: Econometrics [KCH]