The macroeconomic effects of unconventional monetary policy: Comparing euro area and US models with shadow rates

Stefan Hohberger, Marco Ratto and Lukas Vogel

Economic Modelling, 2023, vol. 127, issue C

Abstract: This paper compares the macroeconomic effects of unconventional monetary policy (UMP) measures in the euro area (EA) and the United States (US) in a unified framework. Using shadow-rate estimates to characterise the overall stance of monetary policy, we estimate a large-scale 3-region (EA, US, RoW) DSGE model with data for 1999q1–2019q4, and we perform counterfactual simulations (no UMP) with the short-term policy rate at the effective lower bound (ELB). We find that contributions of UMP to output growth and inflation have the same orders of magnitude in the EA and US (0.1–0.4 pp p.a. for real GDP growth; 0.2–0.7 pp p.a. for CPI inflation). The counterfactual suggests that EA output and price levels would have been 3.4% and 6.7% below actual levels in 2020q4 in the absence of UMP. The earlier and stronger rebound of activity and prices in the US led to US monetary policy normalisation already during 2016–2019.

Keywords: Unconventional monetary policy; DSGE; Shadow rate; Bayesian model estimation (search for similar items in EconPapers)
JEL-codes: C51 E32 E44 E52 F41 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S026499932300250X
Full text for ScienceDirect subscribers only

Related works:
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:127:y:2023:i:c:s026499932300250x

DOI: 10.1016/j.econmod.2023.106438

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 ().