Choosing between identification schemes in noisy-news models

Joshua Chan, Eric Eisenstat and Gary Koop

Studies in Nonlinear Dynamics & Econometrics, 2022, vol. 26, issue 1, 99-136

Abstract: 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 lie in the development of methods for choosing between identification schemes. We estimate specifications with up to 20 variables using US macroeconomic data. We find 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.

Keywords: Bayesian estimation; noise shocks; savage dickey density ratio; structural identification; vector autoregressive moving average models (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/snde-2020-0016

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