Labor Market Volatility in the RBC Search Model: A Look at Hagedorn and Manovskii’s Calibration

Manoj Atolia (), John Gibson () and Milton Marquis

Computational Economics, 2018, vol. 52, issue 2, No 13, 583-602

Abstract: Abstract The standard Diamond–Mortensen–Pissarides (DMP) labor search model generates low volatility in labor market variables relative to average labor productivity, the so-called Shimer puzzle. Hagedorn and Manovskii (Am Econ Rev 98(4):1692–1706, 2008) demonstrate that recalibrating the standard DMP model to be consistent with the small vacancy posting cost and wage elasticity observed in the data can resolve the Shimer puzzle. They close by stating that their calibration strategy would also resolve the Shimer puzzle in the real business cycle (RBC) search framework. In this paper, we examine their claim and find that their strategy resolves the Shimer puzzle in the RBC search model for linear preferences (with risk neutrality and infinite Frisch elasticity of labor supply), but falls significantly short for more standard assumptions on the degree of relative risk aversion (of 1–2) and Frisch elasticity (of 2–3), in line with empirical estimates. While our conclusions are based on highly accurate solutions using the Generalized Stochastic Simulation Algorithm, we also assess the accuracy of a frequently used linearization method and its implications for the assessment of labor market volatility.

Keywords: GSSA; Labor market search; Shimer puzzle; Real business cycle model (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9701-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:52:y:2018:i:2:d:10.1007_s10614-017-9701-9

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-017-9701-9

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().