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Modeling and Policy Analysis for the U.S. Science Sector

Author

Listed:
  • Jacques Kibambe Ngoie

    (Department of Economics, University of Pretoria)

  • Arnold Zellner

    (Booth School of Business, University of Chicago)

Abstract

This paper analyzes the production process of scientific outputs and its implications on the U.S. economy using variants of a disaggregated Marshallian Macroeconomic Model (MMM). Federal spending on scientific activities produces innovation which we measure using the number of patents awarded. Additionally, this study makes use of the Bass diffusion model to investigate how innovative patents generate new products that attract new firms in existing sectors of the U.S. economy. Firms are assumed to be Bayesian learners while forming expectations about product prices. Using a set of policy simulations, this research provides measured information on how selected science policies may affect sectoral growth of the U.S. economy. Moreover, issues such as bifurcation pertaining to dynamic models are thoroughly addressed in this paper. Among others, our findings suggest that federal spending on applied research has larger shortrun growth enhancement effects than spending on development or basic research. The return of current federal spending on applied research depends largely on past spending on basic research, something that is well captured through the lag structure imposed in our model. Recipients of federal grants for basic research often lay foundation for outstanding applied research.

Suggested Citation

  • Jacques Kibambe Ngoie & Arnold Zellner, 2012. "Modeling and Policy Analysis for the U.S. Science Sector," Working Papers 201207, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201207
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    References listed on IDEAS

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    1. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    2. William Barnett & Evgeniya Duzhak, 2010. "Empirical assessment of bifurcation regions within New Keynesian models," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 45(1), pages 99-128, October.
    3. repec:fth:harver:1473 is not listed on IDEAS
    4. Barro, Robert J, 1990. "Government Spending in a Simple Model of Endogenous Growth," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 103-126, October.
    5. Zellner, Arnold & Tobias, Justin, 1998. "A Note on Aggregation, Disaggregation and Forecasting Performance," CUDARE Working Papers 198677, University of California, Berkeley, Department of Agricultural and Resource Economics.
    6. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343, National Bureau of Economic Research, Inc.
    7. Taylor, John B, 1979. "Estimation and Control of a Macroeconomic Model with Rational Expectations," Econometrica, Econometric Society, vol. 47(5), pages 1267-1286, September.
    8. Zellner, Arnold & Chen, Bin, 2001. "Bayesian Modeling Of Economies And Data Requirements," Macroeconomic Dynamics, Cambridge University Press, vol. 5(5), pages 673-700, November.
    9. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    10. Zellner, Arnold & Israilevich, Guillermo, 2005. "The Marshallian macroeconomic model: A progress report," International Journal of Forecasting, Elsevier, vol. 21(4), pages 627-645.
    11. Veloce, William & Zellner, Arnold, 1985. "Entry and empirical demand and supply analysis for competitive industries," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 459-471.
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    More about this item

    Keywords

    Disaggregated Marshallian Macroeconomic Model; Bass Diffusion Model; Transfer Functions; and Bayesian Learners;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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