Pigouvian Cycles

Leonardo Melosi and Renato Faccini

No 13370, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Low-frequency variations in current and expected unemployment rates are important to identify TFP news shocks and to allow a general equilibrium rational expectations model to generate Pigouvian cycles: a large fraction of the comovement of output, consumption, investment, employment, and real wages is explained by changes in expectations unrelated to TFP fundamentals. The model predicts that the start (end) of most U.S. recessions is associated with agents realizing that previous enthusiastic (lukewarm) expectations about future TFP would not be met.

Keywords: Identification of shocks; Tfp news; Noise shocks; The great recession; Bayesian estimation; Labor market trends; Employment gap (search for similar items in EconPapers)
JEL-codes: C11 C51 E32 (search for similar items in EconPapers)
Date: 2018-12
New Economics Papers: this item is included in nep-dge and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://cepr.org/publications/DP13370 (application/pdf)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

Related works:
Journal Article: Pigouvian Cycles (2022)
Working Paper: Pigouvian Cycles (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:13370

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP13370

Access Statistics for this paper

More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().