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Product Innovation with Lumpy Investment

Author

Listed:
  • Chahim, M.

    (Tilburg University, School of Economics and Management)

  • Grass, D.
  • Hartl, R.F.
  • Kort, P.M.

    (Tilburg University, School of Economics and Management)

Abstract

The paper provides a framework that enables us to analyze the important topic of capital accumulation under technological progress. We describe an algorithm to solve Impulse Control problems, based on a (multipoint) boundary value problem approach. Investment takes place in lumps and we determine the optimal timing of technology adoptions as well as the size of the corresponding investments. Our numerical approach led to some guidelines for new technology investments. First, we find that investments are larger and occur in a later stadium when more of the old capital stock needs to be scrapped. Moreover, we obtain that the size of the firm’s investments increase when the technology produces more profitable products. We see that the firm in the beginning of the planning period adopts new technologies faster as time proceeds, but later on the opposite happens. Furthermore, we find that the firm does not invest such that marginal profit is zero, but instead marginal profit is negative.
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(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Chahim, M. & Grass, D. & Hartl, R.F. & Kort, P.M., 2012. "Product Innovation with Lumpy Investment," Other publications TiSEM f68a548c-8246-4e8c-bc75-8, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:f68a548c-8246-4e8c-bc75-8de019177735
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    File URL: https://pure.uvt.nl/ws/portalfiles/portal/1443763/2012-074.pdf
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    Other versions of this item:

    • M. Chahim & D. Grass & R. F. Hartl & P. M. Kort, 2017. "Product innovation with lumpy investment," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 159-182, March.

    References listed on IDEAS

    as
    1. Boucekkine, Raouf & Saglam, Cagri & Valléee, Thomas, 2004. "Technology Adoption Under Embodiment: A Two-Stage Optimal Control Approach," Macroeconomic Dynamics, Cambridge University Press, vol. 8(2), pages 250-271, April.
    2. Feichtinger, Gustav & Hartl, Richard F. & Kort, Peter M. & Veliov, Vladimir M., 2006. "Anticipation effects of technological progress on capital accumulation: a vintage capital approach," Journal of Economic Theory, Elsevier, vol. 126(1), pages 143-164, January.
    3. Grass, D. & Chahim, M., 2012. "Numerical Algorithms for Deterministic Impulse Control Models with Applications," Discussion Paper 2012-081, Tilburg University, Center for Economic Research.
    4. Luhmer, Alfred, 1986. "A continuous time, deterministic, nonstationary model of economic ordering," European Journal of Operational Research, Elsevier, vol. 24(1), pages 123-135, January.
    5. Greenwood, Jeremy & Hercowitz, Zvi & Krusell, Per, 1997. "Long-Run Implications of Investment-Specific Technological Change," American Economic Review, American Economic Association, vol. 87(3), pages 342-362, June.
    6. Dieter Grass & Richard F. Hartl & Peter M. Kort, 2012. "Capital Accumulation and Embodied Technological Progress," Journal of Optimization Theory and Applications, Springer, vol. 154(2), pages 588-614, August.
    7. Suresh P. Sethi, 2021. "Optimal Control Theory," Springer Texts in Business and Economics, Springer, edition 4, number 978-3-030-91745-6, October.
    8. Chahim, Mohammed & Hartl, Richard F. & Kort, Peter M., 2012. "A tutorial on the deterministic Impulse Control Maximum Principle: Necessary and sufficient optimality conditions," European Journal of Operational Research, Elsevier, vol. 219(1), pages 18-26.
    9. Grass, D., 2012. "Numerical computation of the optimal vector field: Exemplified by a fishery model," Journal of Economic Dynamics and Control, Elsevier, vol. 36(10), pages 1626-1658.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Sadana, Utsav & Reddy, Puduru Viswanadha & Zaccour, Georges, 2021. "Nash equilibria in nonzero-sum differential games with impulse control," European Journal of Operational Research, Elsevier, vol. 295(2), pages 792-805.

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    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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