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Economic forecasting / Graham Elliott and Allan Timmermann.

By: Contributor(s): Material type: TextTextPublication details: Princeton : Princeton University Press, ©2016.Description: xiv, 552 pages : illustrations ; 27 cmISBN:
  • 9780691140131
Subject(s): DDC classification:
  • 330.0112 23 El46
Contents:
1. Introduction -- 2. Loss functions -- 3. The parametric forecasting problem -- 4. Classical estimation of forecasting models -- 5. Bayesian forecasting methods -- 6. Model selection -- 7. Univariate linear prediction models -- 8. Univariate nonlinear prediction models -- 9. Vector autoregressions -- 10. Forecasting in a data-rich environment -- 11. Nonparametric forecasting methods -- 12. Binary forecasts -- 13. Volatility and density forecasting -- 14. Forecast combinations -- 15. Desirable properties of forecasts -- 16. Evaluation of individual forecasts -- 17. Evaluation and comparison of multiple forecasts -- 18. Evaluating density forecasts -- 19. Forecasting under model instability -- 20. Trending variables and forecasting -- 21. Forecasting nonstandard data.
Summary: This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance.
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Includes bibliographical references and index.

1. Introduction --
2. Loss functions --
3. The parametric forecasting problem --
4. Classical estimation of forecasting models --
5. Bayesian forecasting methods --
6. Model selection --
7. Univariate linear prediction models --
8. Univariate nonlinear prediction models --
9. Vector autoregressions --
10. Forecasting in a data-rich environment --
11. Nonparametric forecasting methods --
12. Binary forecasts --
13. Volatility and density forecasting --
14. Forecast combinations --
15. Desirable properties of forecasts --
16. Evaluation of individual forecasts --
17. Evaluation and comparison of multiple forecasts --
18. Evaluating density forecasts --
19. Forecasting under model instability --
20. Trending variables and forecasting --
21. Forecasting nonstandard data.

This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance.

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