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

J.H. Stock and Mark Watson

Chapter Chapter 8 in Handbook of Macroeconomics, 2016, vol. 2, pp 415-525 from Elsevier

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.

Keywords: State-space models; Structural vector autoregressions; Factor-augmented vector autoregressions; Principal components; Large-model forecasting; Nowcasting; Structural shocks; C32; C38; C55; E17; E37; E47 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (251)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:macchp:v2-415

DOI: 10.1016/bs.hesmac.2016.04.002

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