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  • Cited by 397
Cambridge University Press
Online publication date:
November 2017
Print publication year:
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Book 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.


'The book by Kilian and Lütkepohl 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 Pennsylvania

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