Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics

J. H. Stock, M. W. Watson

Research output: Chapter in Book/Report/Conference proceedingChapter

70 Scopus citations

Abstract

This chapter provides an overview of and user's guide to dynamic factor models (DFMs), their estimation, and their uses in empirical macroeconomics. It also surveys recent developments in methods for identifying and estimating SVARs, an area that has seen important developments over the past 15 years. The chapter begins by introducing DFMs and the associated statistical tools, both parametric (state-space forms) and nonparametric (principal components and related methods). After reviewing two mature applications of DFMs, forecasting and macroeconomic monitoring, the chapter lays out the use of DFMs for analysis of structural shocks, a special case of which is factor-augmented vector autoregressions (FAVARs). A main focus of the chapter is how to extend methods for identifying shocks in structural vector autoregression (SVAR) to structural DFMs. The chapter provides a unification of SVARs, FAVARs, and structural DFMs and shows both in theory and through an empirical application to oil shocks how the same identification strategies can be applied to each type of model.

Original languageEnglish (US)
Title of host publicationHandbook of Macroeconomics, 2016
EditorsJohn B. Taylor, Harald Uhlig
PublisherElsevier B.V.
Pages415-525
Number of pages111
ISBN (Print)9780444594877
DOIs
StatePublished - 2016

Publication series

NameHandbook of Macroeconomics
Volume2
ISSN (Print)1574-0048

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics
  • Economics, Econometrics and Finance (miscellaneous)

Keywords

  • Factor-augmented vector autoregressions
  • Large-model forecasting
  • Nowcasting
  • Principal components
  • State-space models
  • Structural shocks
  • Structural vector autoregressions

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