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Identifying noise shocks

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  • Benati, Luca
  • Chan, Joshua
  • Eisenstat, Eric
  • Koop, Gary

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 (TFP)—with great precision. In an empirical application, evidence suggests that TFP noise shocks play a minor role in macroeconomic fluctuations.

Suggested Citation

  • Benati, Luca & Chan, Joshua & Eisenstat, Eric & Koop, Gary, 2020. "Identifying noise shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    • Handle: RePEc:eee:dyncon:v:111:y:2020:i:c:s0165188919301770
    DOI: 10.1016/j.jedc.2019.103780
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    Cited by:

    1. Luca Benati & Thomas A. Lubik, 2021. "Searching for Hysteresis," Working Paper 21-03, Federal Reserve Bank of Richmond.
    2. Ansgar Belke & Steffen Elstner & Svetlana Rujin, 2022. "Growth Prospects and the Trade Balance in Advanced Economies," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1209-1234, October.
    3. Luca Gambetti & Christoph Görtz & Dimitris Korobilis & John D. Tsoukalas & Francesco Zanetti, 2022. "The Effect of News Shocks and Monetary Policy," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 139-164, Emerald Group Publishing Limited.

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