Determinants of the duration of the voluntary administration process: An unconditional quantile regression analysis

John Goodwin and James Routledge

Journal of Contemporary Accounting and Economics, 2021, vol. 17, issue 3

Abstract: This study explores determinants of voluntary administration (VA) and deed of company arrangement (DOCA) durations using unconditional quantile regression (UQR). Determinants’ effects are heterogeneous across the VA and DOCA distributions. Determinants related to complexity and negotiation, including size and debt restructuring existence, are positively and negatively related respectively to VA duration, and are stronger at longer durations. Insolvency firm expertise is negatively related to VA duration at shorter durations. Determinants related to scale and procedure, including size and accounting problems, are positively related to DOCA duration, and are stronger at longer durations. No determinants explain short DOCA durations. The UQR results uncovered other new empirical regularities.

Keywords: Insolvency; Duration determinants; Unconditional quantile regression (search for similar items in EconPapers)
JEL-codes: G33 M41 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1815566921000345
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:jocaae:v:17:y:2021:i:3:s1815566921000345

DOI: 10.1016/j.jcae.2021.100276

Access Statistics for this article

Journal of Contemporary Accounting and Economics is currently edited by Agnes C.S. Cheng, P. Clarkson, F.A. Gul, Zoltan Matolcsy, Dan Simunic and Ben Srinidhi

More articles in Journal of Contemporary Accounting and Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().