Table of Contents

Handbook of Research Methods and Applications in Empirical Macroeconomics

Handbook of Research Methods and Applications in Empirical Macroeconomics

Handbooks of Research Methods and Applications series

Edited by Nigar Hashimzade and Michael A. Thornton

This comprehensive Handbook presents the current state of art in the theory and methodology of macroeconomic data analysis. It is intended as a reference for graduate students and researchers interested in exploring new methodologies, but can also be employed as a graduate text. The Handbook concentrates on the most important issues, models and techniques for research in macroeconomics, and highlights the core methodologies and their empirical application in an accessible manner. Each chapter is largely self-contained, whilst the comprehensive introduction provides an overview of the key statistical concepts and methods. All of the chapters include the essential references for each topic and provide a sound guide for further reading.

Chapter 22: Structural vector autoregressions

Lutz Kilian

Subjects: economics and finance, econometrics, research methods, research methods in economics


Notwithstanding the increased use of estimated dynamic stochastic general equilibrium (DSGE) models over the last decade, structural vector autoregressive (VAR) models continue to be the workhorse of empirical macroeconomics and finance. Structural VAR models have four main applications. First, they are used to study the expected response of the model variables to a given one- time structural shock. Second, they allow the construction of forecast error variance decompositions that quantify the average contribution of a given structural shock to the variability of the data. Third, they can be used to provide historical decompositions that measure the cumulative contribution of each structural shock to the evolution of each variable over time. Historical decompositions are essential, for example, in understanding the genesis of recessions or of surges in energy prices (see, for example, Edelstein and Kilian, 2009; Kilian and Murphy, 2013). Finally, structural VAR models allow the construction of forecast scenarios conditional on hypothetical sequences of future structural shocks (see, for example, Waggoner and Zha, 1999; Baumeister and Kilian, 2012). VAR models were first proposed by Sims (1980a) as an alternative to traditional large- scale dynamic simultaneous equation models. Sims’ research program stressed the need to dispense with ad hoc dynamic exclusion restrictions in regression models and to discard empirically implausible exogeneity assumptions.

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