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.
Provides a comprehensive and up-to-date treatment of the topic, allowing readers to find all related methods in one source
Bridges the gap between the technical structural vector autoregressive (VAR) literature and the needs of empirical researchers
Shows the pros and cons of specific structural (VAR) methods, allowing empirical researchers to decide on the most appropriate methods for their analysis.
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
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