Choosing between identification schemes in noisy-news models

Joshua Chan, Eric Eisenstat, Gary Koop

Research output: Contribution to journalArticlepeer-review


This paper is about identifying structural shocks in noisy-news models using structural vector autoregressive moving average (SVARMA) models. We develop a new identification scheme and efficient Bayesian methods for estimating the resulting SVARMA. We discuss how our identification scheme differs from the one which is used in existing theoretical and empirical models. Our main contributions lies in the development of methods for choosing between identification schemes. We estimate specifications with up to 20 variables using US macroeconomic data. We nd that our identification scheme is preferred by the data, particularly as the size of the system is increased and that noise shocks generally play a negligible role. However, small models may overstate the importance of noise shocks.
Original languageEnglish
JournalStudies in Nonlinear Dynamics and Econometrics
Publication statusAccepted/In press - 21 Sep 2020


  • noisy-news models
  • structural vector autoregressive moving average (SVARMA)
  • Bayesian methods


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