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Transition Dynamics in Endogenous Recombinant Growth Models by Means of Projection Methods

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  • Fabio Privileggi

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Abstract

This paper provides a step further in the computation of the transition path of a continuous time endogenous growth model discussed by Privileggi (2010) – based on the setting first introduced by Tsur and Zemel (2007) – in which knowledge evolves according to the Weitzman (1998) recombinant process. A projection method, based on the least squares of the residual function corresponding to the ODE defining the optimal policy of the 'detrended' model, allows for the numeric approximation of such policy for a positive Lebesgue measure range of values of the efficiency parameter characterizing the probability function of the recombinant process. Although the projection method's performance rapidly degenerates as one departs from a benchmark value for the efficiency parameter, we are able to numerically compute time-path trajectories which are sufficiently regular to allow for sensitivity analysis under changes in parameters' values.
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Suggested Citation

  • 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.
  • Handle: RePEc:kap:compec:v:38:y:2011:i:3:p:367-387
    DOI: 10.1007/s10614-011-9278-7
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    File URL: http://hdl.handle.net/10.1007/s10614-011-9278-7
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    References listed on IDEAS

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    1. de la Grandville,Olivier, 2009. "Economic Growth," Cambridge Books, Cambridge University Press, number 9780521725200, December.
    2. de la Grandville,Olivier, 2009. "Economic Growth," Cambridge Books, Cambridge University Press, number 9780521898010, December.
    3. Martin L. Weitzman, 1998. "Recombinant Growth," The Quarterly Journal of Economics, Oxford University Press, vol. 113(2), pages 331-360.
    4. Privileggi, Fabio, 2008. "On the transition dynamics in endogenous recombinant growth models," POLIS Working Papers 120, Institute of Public Policy and Public Choice - POLIS.
    5. Ellen R. McGrattan, 1998. "Application of weighted residual methods to dynamic economic models," Staff Report 232, Federal Reserve Bank of Minneapolis.
    6. Tsur, Yacov & Zemel, Amos, 2007. "Towards endogenous recombinant growth," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3459-3477, November.
    7. Marimon, Ramon & Scott, Andrew (ed.), 1999. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, number 9780198294979.
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    Citations

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    Cited by:

    1. 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.
    2. repec:uto:dipeco:201338 is not listed on IDEAS
    3. Marchese, Carla & Marsiglio, Simone & Privileggi, Fabio & Ramello, Giovanni B., 2017. "Endogenous Recombinant Growth and Intellectual Property Rights," IEL Working Papers 23, Institute of Public Policy and Public Choice - POLIS.
    4. 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.
    5. Privileggi, Fabio, 2015. "Takeoff vs. stagnation in endogenous recombinant growth models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 184-214.

    More about this item

    Keywords

    Knowledge production; Endogenous recombinant growth; Transition dynamics; Projection methods; Least squares; C61; C63; O31; O41;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • 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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