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This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields. Inhaltsverzeichnis 1. Introduction; 2. Vector autoregressive models; 3. Vector error correction models; 4. Structural VAR tools; 5. Bayesian VAR analysis; 6. The relationship between VAR models and other macroeconometric models; 7. A historical perspective on causal inference in macroeconometrics; 8. Identification by short-run restrictions; 9. Estimation subject to short-run restrictions; 10. Identification by long-run restrictions; 11. Estimation subject to long-run restrictions; 12. Inference in models identified by short-run or long-run restrictions; 13. Identification by sign restrictions; 14. Identification by heteroskedasticity or non-gaussianity; 15. Identification based on extraneous data; 16. Structural VAR analysis in a data-rich environment; 17. Nonfundamental shocks; 18. Nonlinear structural VAR models; 19. Practical issues related to trends, seasonality, and structural change; References; Index.
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Structural Vector Autoregressive Analysis, Lutz Kilian, Helmut Lütkepohl
- Language
- Released
- 2018
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- (Hardcover)
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- Title
- Structural Vector Autoregressive Analysis
- Language
- English
- Authors
- Lutz Kilian, Helmut Lütkepohl
- Publisher
- Cambridge University Press
- Released
- 2018
- Format
- Hardcover
- Pages
- 756
- ISBN13
- 9781107196575
- Category
- Business and Economics
- Description
- This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields. Inhaltsverzeichnis 1. Introduction; 2. Vector autoregressive models; 3. Vector error correction models; 4. Structural VAR tools; 5. Bayesian VAR analysis; 6. The relationship between VAR models and other macroeconometric models; 7. A historical perspective on causal inference in macroeconometrics; 8. Identification by short-run restrictions; 9. Estimation subject to short-run restrictions; 10. Identification by long-run restrictions; 11. Estimation subject to long-run restrictions; 12. Inference in models identified by short-run or long-run restrictions; 13. Identification by sign restrictions; 14. Identification by heteroskedasticity or non-gaussianity; 15. Identification based on extraneous data; 16. Structural VAR analysis in a data-rich environment; 17. Nonfundamental shocks; 18. Nonlinear structural VAR models; 19. Practical issues related to trends, seasonality, and structural change; References; Index.