Structural Vector Autoregressive Analysis

This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.

Lutz Kilian (Author), Helmut Lütkepohl (Author)

9781107196575, Cambridge University Press

Hardback, published 23 November 2017

754 pages, 40 b/w illus.
23.5 x 15.7 x 4.5 cm, 1.14 kg

'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

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.

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.

Subject Areas: Economic statistics [KCHS], Econometrics [KCH], Macroeconomics [KCB], Economics [KC]