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Identifying Noise Shocks: A VAR with Data Revisions
Author(s) -
MASOLO RICCARDO M.,
PACCAGNINI ALESSIA
Publication year - 2019
Publication title -
journal of money, credit and banking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.763
H-Index - 108
eISSN - 1538-4616
pISSN - 0022-2879
DOI - 10.1111/jmcb.12585
Subject(s) - vector autoregression , shock (circulatory) , noise (video) , hindsight bias , econometrics , exploit , identification (biology) , structural vector autoregression , economics , computer science , macroeconomics , monetary policy , artificial intelligence , computer security , medicine , psychology , botany , cognitive psychology , image (mathematics) , biology
We propose a new Vector Autoregression (VAR) identification strategy to study the impact of noise, in the early releases of output growth figures, which exploits the informational advantage of the econometrician. Economic agents, uncertain about the underlying state of the economy, respond to noisy early data releases. Econometricians, with the benefit of hindsight, have access to data revisions as well, which we use to identify noise shocks. A surprising report of output growth produces qualitatively similar but quantitatively smaller effects than a demand shock. We also illustrate how a noise shock cannot be identified unless ex‐post information is used.