Identifying noise shocks

Luca Benati, Joshua Chan, Eric Eisenstat, Gary Koop

Research output: Contribution to journalArticle

Abstract

We study identifying restrictions that allow news and noise shocks to be recovered empirically within a Bayesian structural VARMA framework. In population, the identification scheme we consider exactly recovers news and noise shocks. Monte Carlo evidence further demonstrates its excellent performance, as it recovers the key features of the postulated data-generation process–the real-business cycle model of Barsky and Sims (2011) augmented with noise shocks about future total factor productivity-with great precision. We provide several empirical applications of our identification scheme. Evidence uniformly support the conclusion that noise shocks play a minor role in macroeconomic fluctuations.
Original languageEnglish
Article number103780
Number of pages17
JournalJournal of Economic Dynamics and Control
Volume111
Early online date5 Nov 2019
DOIs
Publication statusPublished - 29 Feb 2020

Keywords

  • VARMA framework
  • noise shocks
  • Monte Carlo method

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