Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate

Chun-I Chen

Chaos, Solitons & Fractals, 2008, vol. 37, issue 1, 278-287

Abstract: The grey model is characterized by basic mathematics and a need for less raw data, but it also lacks the flexibility to adjust the model to increase the precision for the forecasting model. This study investigates forecasting using novel nonlinear grey Bernoulli model (NGBM). The NGBM is a nonlinear differential equation with power n. The curvature of the solution curve can be adjusted according to the observed first time accumulated generating operation of raw data by properly choosing power n. The power n is determined using a simple computer program, which calculates the minimum average relative percentage error of the forecast model. The NGBM is applied to re-examine an example in Deng’s book and the analytical results demonstrate that it effectively enhances the modeling precision. The model precision can be increased owing to the nonlinearity of natural phenomena. The novel NGBM then is applied to forecast the annual unemployment rate of 10 selected countries for 2006. The modelling results help governments to develop future policies regarding labor and economic policies.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077906008654
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:chsofr:v:37:y:2008:i:1:p:278-287

DOI: 10.1016/j.chaos.2006.08.024

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().