Structural vector autoregressive analysis
Author(s)
Bibliographic Information
Structural vector autoregressive analysis
（Themes in modern econometrics）
Cambridge University Press, 2017
- : hardback
- : pbk
Available at / 26 libraries
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National Graduate Institute for Policy Studies Library (GRIPS Library)
: pbk331.19||Ki2901447173
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Note
Some copies have different pagination: xx, 735 p.
Includes bibliographical references (p. 673-712) and indexes
Description and Table of Contents
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.
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.
by "Nielsen BookData"
