A Bayesian estimation of a stochastic predator-prey model of economic fluctuations

Ghassan Dibeh; Dmitry G. Luchinsky; Daria D. Luchinskaya; Vadim N. Smelyanskiy

doi:10.1117/12.724764

15 June 2007 A Bayesian estimation of a stochastic predator-prey model of economic fluctuations

Ghassan Dibeh, Dmitry G. Luchinsky, Daria D. Luchinskaya, Vadim N. Smelyanskiy

Proceedings Volume 6601, Noise and Stochastics in Complex Systems and Finance; 660115 (2007) https://doi.org/10.1117/12.724764
Event: SPIE Fourth International Symposium on Fluctuations and Noise, 2007, Florence, Italy

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.

Citation Download Citation

Ghassan Dibeh, Dmitry G. Luchinsky, Daria D. Luchinskaya, and Vadim N. Smelyanskiy "A Bayesian estimation of a stochastic predator-prey model of economic fluctuations", Proc. SPIE 6601, Noise and Stochastics in Complex Systems and Finance, 660115 (15 June 2007); https://doi.org/10.1117/12.724764

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