Identifying Noise Shocks: A VAR with Data Revisions

Abstract

We propose a new VAR identification strategy to study the impact of noise shocks on aggregate activity. We do so exploiting the informational advantage the econometrician has, relative to the economic agent. The latter, who is uncertain about the underlying state of the economy, responds to the noisy early data releases. The former, with the benefit of hindsight, has access to data revisions as well, which can be used to identify noise shocks. By using a VAR we can avoid making very specific assumptions on the process driving data revisions. We rather remain agnostic about it but make our identification strategy robust to whether data revisions are driven by noise or news. Our analysis shows that a surprising report of output growth numbers delivers a persistent and hump-shaped response of real output and unemployment. The responses are qualitatively similar but an order of magnitude smaller than those to a demand shock. Finally, our counterfactual analysis supports the view that it would not be possible to identify noise shocks unless different vintages of data are used.

Animal Spirits, Fundamental Factors and Business Cycle Fluctuations

“…In this paper we show that our methodology can be used to approximately identify supply and noise shocks in more general models, such as that of Blanchard et al (2013), in which the true state of economy can never be retrieved. Masolo and Paccagnini (2018) identify noise shocks by exploiting the informational advantage of the econometricians, who have access, with the benefit of hindsight, to data revisions as well. While we also use the noisy early data releases to identify the noise shocks, we also base our identification of agents' misperceptions about the future using forecast errors regarding trend output.…”