The Goodwin model is a widely used economic growth model able to explain endogenous fluctuations in employment rate andwage share; in its initial version, the standard Phillips curve is used. In the present work, we suggest a revised Phillips curve that takes into account how the wage share influences the rate of changes of the wage itself thus obtaining a continuous-time modified Goodwin model. Since applying models to real data often requires working in a discrete-time setup, we then move from the continuous-time to the discrete-time version of the proposed model, by using a general polynomial discretization method in backward and forward-looking (hybrid discretization). By comparing the continuous-time system to its discrete-time counterpart we prove that fixed points and local dynamics do not change, as long as the time step is not too high. Moreover, numerical simulations employing Dynamic Time Warping, cross-correlation, and semblance analysis consistently affirm that enhancing the similarity of quantitative dynamics is achieved by reducing the time step.

A dynamically consistent discretization method for the Goodwin model with nonlinear Phillips curve. Comparing qualitative and quantitative dynamics

Baldi, M. M.;Guzowska, M.;Michetti, E.
2024-01-01

Abstract

The Goodwin model is a widely used economic growth model able to explain endogenous fluctuations in employment rate andwage share; in its initial version, the standard Phillips curve is used. In the present work, we suggest a revised Phillips curve that takes into account how the wage share influences the rate of changes of the wage itself thus obtaining a continuous-time modified Goodwin model. Since applying models to real data often requires working in a discrete-time setup, we then move from the continuous-time to the discrete-time version of the proposed model, by using a general polynomial discretization method in backward and forward-looking (hybrid discretization). By comparing the continuous-time system to its discrete-time counterpart we prove that fixed points and local dynamics do not change, as long as the time step is not too high. Moreover, numerical simulations employing Dynamic Time Warping, cross-correlation, and semblance analysis consistently affirm that enhancing the similarity of quantitative dynamics is achieved by reducing the time step.
2024
Springer
Internazionale
https://link.springer.com/epdf/10.1007/s10203-024-00491-9?sharing_token=O3rXPYF8UGFNlOLA-Fu56Pe4RwlQNchNByi7wbcMAY4PXhDkzTm06sbjnOwRcRHBtsl08QiVR9-C4Dt_XCodadr1ntM5puNR4hP4lEp8dJWNwaPZyhuBDAm-7X8zpdZM4ArEpoUB4DPCk5oj91IyyIafDKiS_LllvPMZGVbr8TM=
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11393/342035
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