Modelling the US, the UK and Japanese unemployment rates. Fractional integrationand structural breaks
Luis Gil-Alana and
Guglielmo Maria Caporale
No 11/08, Faculty Working Papers from School of Economics and Business Administration, University of Navarra
Abstract:
A general procedure for fractional integration and structural breaks at unknown points in time is used, which allows for different orders of integration and deterministic components in each subsample. First, the procedure is extended to the non-linear case, and is showed by means of Monte Carlo experiments that it performs well in a non-linear environment. Second, it is applied to test for a single break in the unemployment rate in the US, the UK and Japan. The results shed some light on the empirical relevance of alternative unemployment theories for these countries. Specifically, a structuralist interpretation appears more appropriate for the US and Japan, whilst a hysteresis model accounts better for the UK experience (and also for the Japanese one in the second subsanple). These findings are interpreted in terms of structural instability in labour markets with different features.
Pages: 46 pages
Date: 2008-11-20
New Economics Papers: this item is included in nep-ecm and nep-lab
References: Add references at CitEc
Citations: View citations in EconPapers (39)
Published in COMPUTATIONAL STATISTICS AND DATA ANALYSIS 52, 11, 4998-5013, 2008
Downloads: (external link)
http://www.unav.edu/documents/10174/6546776/122719 ... 8_Alana_Caporale.pdf (application/pdf)
Related works:
Journal Article: Modelling the US, UK and Japanese unemployment rates: Fractional integration and structural breaks (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:una:unccee:wp1108
Access Statistics for this paper
More papers in Faculty Working Papers from School of Economics and Business Administration, University of Navarra
Bibliographic data for series maintained by ().