Structural Vector Autoregressive Analysis
Published on 23. November 2017
Book
Paperback/Softback
754 pages
978-1-316-64733-2 (ISBN)
Description
Structural vector autoregressive (VAR) models are important tools for empirical work in macroeconomics, finance, and related fields. This book not only reviews the many alternative structural VAR approaches discussed in the literature, but also highlights their pros and cons in practice. It provides guidance to empirical researchers as to the most appropriate modeling choices, methods of estimating, and evaluating structural VAR models. The book traces the evolution of the structural VAR methodology and contrasts it with other common methodologies, including dynamic stochastic general equilibrium (DSGE) models. It is intended as a bridge between the often quite technical econometric literature on structural VAR modeling and the needs of empirical researchers. The focus is not on providing the most rigorous theoretical arguments, but on enhancing the reader's understanding of the methods in question and their assumptions. Empirical examples are provided for illustration.
Reviews / Votes
'The book by Kilian and Luetkepohl will become the new benchmark textbook for teaching structural vector autoregressive analysis. This book thus devotes considerable space to the issue of identification, including sign restrictions, to Bayesian methods, to Factor Vector Autoregressions and to non-fundamental shocks. These are key to understanding much of recent research. The authors do an excellent job of assembling and lucidly explaining it all. This book is destined to become a classic.' Harald Uhlig, University of Chicago 'Structural vector autoregressions (SVARs) are an essential tool in empirical macroeconomics. This book provides a thorough and long-overdue digest of a literature that has been thriving for over 35 years and seen a lot of exciting developments in the past decade. The authors masterfully blend theoretical foundations, guidance for practitioners, and detailed empirical applications. This is a must-read for anyone working with SVARs.' Frank Schorfheide, University of PennsylvaniaMore details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Product notice
Paperback (trade)
Illustrations
Worked examples or Exercises; 40 Line drawings, black and white
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 45 mm
Weight
1206 gr
ISBN-13
978-1-316-64733-2 (9781316647332)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Lutz Kilian | Helmut Luetkepohl
Structural Vector Autoregressive Analysis
Book
11/2017
Cambridge University Press
€212.00
Shipment within 15-20 days
Persons
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 Luetkepohl has held professorial positions at Universitaet Hamburg, the Christian-Albrechts-Universitaet zu Kiel, Germany, the Humboldt-Universitaet zu Berlin, the European University Institute, Florence, and the Freie Universitaet Berlin. He has served as Dean of the Graduate Center of the Deutsches Institut fuer 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).
Content
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.