This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
Lutz Kilian is Professor of Economics at the University of Michigan, Ann Arbor. Between 2001 and 2003 he served as an adviser to the European Central Bank in Frankfurt am Main, Germany. Professor Kilian has been a research visitor at the Federal Reserve Board, the Bank of Canada, the European Central Bank, and the International Monetary Fund. His work has appeared in Econometrica, the American Economic Review, and the Journal of Political Economy. He has served as associate editor of the Journal of Business and Economic Statistics, among other journals.
Inhaltsangabe
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.
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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