Is Price Level Targeting a Robust Monetary Rule?

Szabolcs Deak, Paul Levine (), Afrasiab Mirza and Joseph Pearlman
Additional contact information
Afrasiab Mirza: Department of Economics, University of Birmingham

No 2104, Discussion Papers from University of Exeter, Department of Economics

Abstract: We study the design of monetary policy rules robust to model uncertainty across a set of well-established DSGE models with varied financial frictions. In our novel forward-looking approach, policymakers weight models based on relative forecasting performance. We find that models with frictions between households and banks forecast best during periods of financial turmoil while those with frictions between banks and firms perform best during tranquil periods. However, a model without financial frictions performs nearly as well as models with financial frictions on average. The optimal robust policy is close to a price-level rule which is key when facing uncertainty over the nature of financial frictions.

Keywords: Bayesian estimation; DSGE models; financial frictions; forecasting; prediction pools; optimal simple rules (search for similar items in EconPapers)
JEL-codes: C9 D18 D91 Z1 (search for similar items in EconPapers)
Date: 2021-08-10
New Economics Papers: this item is included in nep-cwa, nep-dge, nep-isf, nep-mon and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://exetereconomics.github.io/RePEc/dpapers/DP2104.pdf (application/pdf)

Related works:
Working Paper: Is Price Level Targeting a Robust Monetary Rule? (2020)
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:exe:wpaper:2104

Access Statistics for this paper

More papers in Discussion Papers from University of Exeter, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sebastian Kripfganz ().