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Proceedings Paper

A Bayesian estimation of a stochastic predator-prey model of economic fluctuations
Author(s): Ghassan Dibeh; Dmitry G. Luchinsky; Daria D. Luchinskaya; Vadim N. Smelyanskiy
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Paper Abstract

In this paper, we develop a Bayesian framework for the empirical estimation of the parameters of one of the best known nonlinear models of the business cycle: The Marx-inspired model of a growth cycle introduced by R. M. Goodwin. The model predicts a series of closed cycles representing the dynamics of labor's share and the employment rate in the capitalist economy. The Bayesian framework is used to empirically estimate a modified Goodwin model. The original model is extended in two ways. First, we allow for exogenous periodic variations of the otherwise steady growth rates of the labor force and productivity per worker. Second, we allow for stochastic variations of those parameters. The resultant modified Goodwin model is a stochastic predator-prey model with periodic forcing. The model is then estimated using a newly developed Bayesian estimation method on data sets representing growth cycles in France and Italy during the years 1960-2005. Results show that inference of the parameters of the stochastic Goodwin model can be achieved. The comparison of the dynamics of the Goodwin model with the inferred values of parameters demonstrates quantitative agreement with the growth cycle empirical data.

Paper Details

Date Published: 15 June 2007
PDF: 9 pages
Proc. SPIE 6601, Noise and Stochastics in Complex Systems and Finance, 660115 (15 June 2007); doi: 10.1117/12.724764
Show Author Affiliations
Ghassan Dibeh, Lebanese American Univ. (Lebanon)
Dmitry G. Luchinsky, NASA Ames Research Ctr. (United States)
Mission Critical Technologies Inc. (United States)
Daria D. Luchinskaya, Univ. of Oxford (United Kingdom)
Vadim N. Smelyanskiy, NASA Ames Research Ctr. (United States)

Published in SPIE Proceedings Vol. 6601:
Noise and Stochastics in Complex Systems and Finance
János Kertész; Stefan Bornholdt; Rosario N. Mantegna, Editor(s)

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