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Forecasting Non-Stationary Economic Time Series

Опубликовано на портале: 02-03-2004
Cambridge, Mass: MIT Press, 2001
Тематический раздел:
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.

List of figures
List of tables
Preface
Common acronyms
    1. An introduction to forecasting
    2. First principles
    3. Evaluating forecast accuracy
    4. Forecasting in univariate processes
    5. Monte Carlo techniques
    6. Forecasting in cointegrated systems
    7. Forecasting with large-scale macroeconometric models
    8. A theory of intercept correction: beyond mechanistic forecasts
    9. Forecasting using leading indicators
    10. Combining forecasts
    11. Multi-step estimation
    12. Parsimony
    13. Testing forecast accuracy
    14. Postscript

Glossary
References
Author index
Subject index


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