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Vector autoregressive (VAR) models are among the most widely used
econometric tools in the fields of macroeconomics and financial
economics. Much of what we know about the response of the economy
to macroeconomic shocks and about how various shocks have
contributed to the evolution of macroeconomic and financial
aggregates is based on VAR models. VAR models also have been used
successfully for economic and business forecasting, for modelling
risk and volatility, and for the construction of forecast
scenarios. Since the introduction of VAR models by C.A. Sims in
1980, the VAR methodology has continuously evolved. Even today
important extensions and reinterpretations of the VAR framework are
being developed. Examples include VAR models for mixed-frequency
data, VAR models as approximations to DSGE models, factor-augmented
VAR models, new tools for the identification of structural shocks
in VAR models, panel VAR approaches, and time-varying parameter VAR
models. This volume collects contributions from some of the leading
VAR experts in the world on VAR methods and applications. Each
chapter highlights and synthesizes a new development in this
literature in a way that is accessible to practitioners, to
graduate students, and to readers in other fields.
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.
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.
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