Forecasting an essential introduction
Concise, engaging, and highly intuitive--this accessible guide equips you with an understanding of all the basic principles of forecasting.
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
New Haven ; London :
Yale University Press
[2019]
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| Colección: | EBSCO Academic eBook Collection Complete.
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| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b47423390*spi |
Tabla de Contenidos:
- Preface; Acknowledgments; Chapter 1. Why do we need forecasts?; What is a forecast?; Why do we need forecasts?; A brief history of forecasting; Why are forecasts uncertain?; A motoring analogy; Beware false forecasting; Time, models and the future; The road ahead-literally; Chapter 2. How do we make forecasts?; A galaxy of terms, and ways, for 'seeing into the future'; Making forecasts; Forecasting her journey time; Forecasting in 'normal' times; More uncertainty; Illustrating forecast uncertainty; Adapting to forecast failure; Updating forecasts as time goes by; Sources of information; Chapter 3. Where are we before we forecast?
- The motorist and the economist; Why are data subject to revision? And why might it matter?; Forecasting data revisions; Inaccurate data do matter; Chapter 4. How do we judge forecasts?; Forecasts are made to inform decisions; Standard forecast evaluation criteria; Everyone wins!; Unequal costs of positive and negative forecast errors; Chapter 5. How uncertain are our forecasts?; Modeling and forecasting uncertainty; Interval forecasts; 'Density' forecasts; Evaluating 'density' forecasts; Chapter 6. Are some real world events unpredictable?; Sudden unanticipated shifts: When the ground moves; Flocks of 'black swans'; Trends and their ilk.
- Why does the type of trend matter?; Trends can cancel; Location shifts can also cancel; Chapter 7. Why do systematic forecast failures occur?; Some impressive forecast failures; Missing systematically; We don't always fail!; What changes matter most for forecast failure?; Learning from past mistakes; What do forecast failures entail?; Chapter 8. Can we avoid systematic forecast failures?; The bus-stop game; Risks and benefits of 'causal' models; Adaptation as forecasts go wrong; Why does differencing work?; Robustification can help; Chapter 9. How do we automatically detect breaks?; Finding shifts by indicator saturation.
- Chapter 10. Can we forecast breaks before they hit?; What would we need to know?; Forecasting the Great Recession; The 2004 Indian Ocean tsunami; Two information sets; Chapter 11. Can we improve forecasts during breaks?; Illustrating forecasting during a break; A possible role for non-linear models; Missing breaks, but adapting quickly; Switching between several 'regimes'; The costs of mis-forecasting hurricanes; Forecasting climate after a volcanic eruption; Chapter 12. Would more information be useful?; Pooling information; Are simple models best?; Pooling forecasts; ; Using other information; Should we use big or small forecasting models?
- If only we could forecast shifts!; Chapter 13. Can econometrics improve forecasting?; Models versus extrapolation (or rules-of-thumb); All models are not born equal; Are 'good' forecasting models useful for policy?; From forecasting to forediction; Federal Open Market Committee members' assessments; Chapter 14. Can you trust economic forecasts?; Is economic forecasting an oxymoron?; In need of better communication; Can you believe forecasts from 'experts'?; Chapter 15. Further reading; Subject index.
