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On the transition dynamics in endogenous recombinant growth models

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  • Privileggi, Fabio

Abstract

This paper constitutes a first attempt at studying the transition dynamics of the Tsur and Zemel (2007) continuous time endogenous growth framework in which knowledge evolves according to the Weitzman (1998) recombinant process. For a specific choice of the probability function characterizing the Weitzman recombinant process, we find a suitable transformation for the state and control variables in the dynamical system diverging to asymptotic constant growth, so that an equivalent 'detrended' system converging to a steady state in the long run can be tackled. Since the dynamical system obtained so far turns out to be analytically intractable, we rely on numerical simulation in order to fully describe the transition dynamics for a set of values of the parameters.

Suggested Citation

  • Privileggi, Fabio, 2008. "On the transition dynamics in endogenous recombinant growth models," POLIS Working Papers 120, Institute of Public Policy and Public Choice - POLIS.
    • Handle: RePEc:uca:ucapdv:120
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    Cited by:

    1. Orso, Cristina Elisa, 2009. "Formal and informal sectors: Interactions between moneylenders and traditional banks in the rural Indian credit market," POLIS Working Papers 135, Institute of Public Policy and Public Choice - POLIS.
    2. Marchese, Carla & Marsiglio, Simone & Privileggi, Fabio & Ramello, Giovanni B., 2019. "Endogenous Recombinant Growth And Intellectual Property Rights," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 2035-2067, July.
    3. Marchese, Carla & Marsiglio, Simone & Privileggi, Fabio & Ramello, Giovanni, 2014. "Endogenous Recombinant Growth through Market Production of Knowledge and Intellectual Property Rights," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201413, University of Turin.
    4. repec:uto:dipeco:201338 is not listed on IDEAS
    5. Fabio Privileggi, 2011. "Transition Dynamics in Endogenous Recombinant Growth Models by Means of Projection Methods," Computational Economics, Springer;Society for Computational Economics, vol. 38(3), pages 367-387, October.
    6. Bondonio, Daniele, 2009. "Impact identification strategies for evaluating business incentive programs," POLIS Working Papers 129, Institute of Public Policy and Public Choice - POLIS.
    7. Giuranno, Michele, 2009. "The logic of party coalitions with political activism and public financing," POLIS Working Papers 134, Institute of Public Policy and Public Choice - POLIS.
    8. Privileggi, Fabio & Marsiglio, Simone, 2014. "Dynamics and Welfare in Recombinant Growth Models with Intellectual Property Rights: a Computational Method," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201414, University of Turin.

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    More about this item

    Keywords

    Knowledge Production; Recombinant Expansion Process; Endogenous Balanced Growth; Turnpike; Transition Dynamics;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

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