Price level targeting and risk management

Roberto Billi

Economic Modelling, 2018, vol. 73, issue C, 163-173

Abstract: The desirability of a nominal-level target during zero lower bound (ZLB) episodes has become a relevant topic for central bankers and academics. In such a context, this article studies the effects of uncertainty about the future state of the economy on the performance of strict-price-level targeting versus nominal-GDP-level targeting. These targeting frameworks are compared in a small New Keynesian model, which offers a clear illustration of the tradeoffs faced by the central bank. The analysis shows that uncertainty about the future hampers economic performance to a greater extent under nominal-GDP-level targeting, relative to strict-price-level targeting. The reason is that strict-price-level targeting induces greater policy inertia and, therefore, improves the tradeoffs faced by the central bank during ZLB episodes.

Keywords: Nominal level targets; Optimal policy; Inertial Taylor rule (search for similar items in EconPapers)
JEL-codes: E31 E52 E58 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0264999317314190
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Price Level Targeting and Risk Management (2016)
Working Paper: Price-level targeting and risk management in a low-inflation economy (2008)
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:eee:ecmode:v:73:y:2018:i:c:p:163-173

DOI: 10.1016/j.econmod.2018.03.013

Access Statistics for this article

Economic Modelling is currently edited by S. Hall and P. Pauly

More articles in Economic Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().