Structural Vector Autoregressive Analysis

Structural Vector Autoregressive Analysis

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Product Details

ISBN-13: 9781316647332
Publisher: Cambridge University Press
Publication date: 11/23/2017
Series: Themes in Modern Econometrics
Edition description: New Edition
Pages: 754
Sales rank: 1,008,558
Product dimensions: 5.94(w) x 8.94(h) x 1.61(d)

About the Author

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.

Helmut Lütkepohl has held professorial positions at Universität Hamburg, the Christian-Albrechts-Universität zu Kiel, Germany, the Humboldt-Universität zu Berlin, the European University Institute, Florence, and the Freie Universität Berlin. He has served as Dean of the Graduate Center of the Deutsches Institut für Wirtschaftsforschung, Berlin. He has published professional articles in Econometrica, the Journal of Econometrics, the Journal of Business and Economic Statistics, Econometric Theory, and the Journal of Applied Econometrics. He has also served as associate editor of the Journal of Econometrics, Econometric Theory, Macroeconomic Dynamics, the Journal of Applied Econometrics, and Econometric Reviews. He is the author of New Introduction to Multiple Time Series Analysis (2010).

Table of Contents

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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