Skip to main content
Log in

Optimal Trajectories of the Innovation Process and Their Matching with Econometric Data

  • Published:
Journal of Optimization Theory and Applications Aims and scope Submit manuscript

Abstract

A dynamical model of optimal economic growth is used for the comparison of catalogs of real econometric data and synthetic growth scenarios. The model is calibrated on a database of the Tokyo Institute of Technology. Special attention is paid to the aggregated data of the Japanese manufacturing industry in the period 1960–92. A description of an algorithm modeling optimal trends in the technological dynamics is given.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. TARASYEV, A. M., and WATANABE, C., Optimal Dynamics of Innoûation in Models of Economic Growth, Journal of Optimization Theory and Applications, Vol. 61, pp. 175–203, 2000.

    Google Scholar 

  2. ARROW, K. J., Applications of Control Theory to Economic Growth, Production and Capital: Collected Papers, Belknap Press of Harvard University Press, Cambridge, Massachusetts, Vol. 5, pp. 261–296, 1985.

    Google Scholar 

  3. GROSSMAN, G. M., and HELPMAN, E., Innoûation and Growth in the Global Economy, MIT Press, Cambridge, Massachusetts, 1991.

    Google Scholar 

  4. INTRILIGATOR, M., Mathematical Optimization and Economic Theory, Prentice-Hall, New York, NY, 1971.

    Google Scholar 

  5. LEITMANN, G., and LEE, C. S., On One Aspect of Science Policy Based on an Uncertain Model, Annals of Operations Research, Vol. 88, pp. 199–214, 1999.

    Google Scholar 

  6. PONTRYAGIN, L. S., BOLTYANSKII, V. G., GAMKRELIDZE, R. V., and MISHCHENKO, E. F., The Mathematical Theory of Optimal Processes, Wiley-Interscience, New York, NY, 1962.

    Google Scholar 

  7. KRASOVSKII, A. N., and KRASOVSKII, N. N., Control under Lack of Information, Birkhaüser, Boston, Massachusetts, 1995.

    Google Scholar 

  8. SUBBOTIN, A. I., Generalized Solutions for First-Order PDE, Birkhaüser, Boston, Massachusetts, 1995.

    Google Scholar 

  9. CHERNOUSKO, F. L., State Estimation for Dynamic Systems, CRC Press, Boca Raton, Florida, 1994.

    Google Scholar 

  10. WATANABE, C., Trends in the Substitution of Production Factors to Technology: Empirical Analysis of the Inducing Impact of the Energy Crisis on Japanese Industrial Technology, Research Policy, Vol. 21, pp. 481–505, 1992.

    Google Scholar 

  11. BORISOV, V. F., HUTSCHENREITER, G., and KRYAZHIMSKII, A. V., Asymptotic Growth Rates in Knowledge-Exchanging Economies, Annals of Operations Research, Vol. 89, pp. 61–73, 1999.

    Google Scholar 

  12. DOLCETTA, I. C., On a Discrete Approximation of the Hamilton-Jacobi Equation of Dynamic Programming, Applied Mathematics and Optimization, Vol. 10, pp. 367–377, 1983.

    Google Scholar 

  13. FEICHTINGER, G., and WIRL, F., Instabilities in Concaûe, Dynamic, Economic Optimization, Journal of Optimization Theory and Applications, Vol. 107, pp. 277–288, 2000.

    Google Scholar 

  14. TARASYEV, A. M., Control Synthesis in Grid Schemes for Hamilton-Jacobi Equations, Annals of Operations Research, Vol. 88, pp. 337–359, 1999.

    Google Scholar 

  15. HARTMAN, P. H., Ordinary Differential Equations, J. Wiley and Sons, New York, NY, 1964.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Reshmin, S., Tarasyev, A. & Watanabe, C. Optimal Trajectories of the Innovation Process and Their Matching with Econometric Data. Journal of Optimization Theory and Applications 112, 639–655 (2002). https://doi.org/10.1023/A:1017924301798

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1017924301798

Navigation