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

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Author Info

  • 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.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201207.

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Length: 20 pages
Date of creation: Feb 2012
Date of revision:
Handle: RePEc:pre:wpaper:201207

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Keywords: Disaggregated Marshallian Macroeconomic Model; Bass Diffusion Model; Transfer Functions; and Bayesian Learners;

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References

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  1. Frank Smets & Raf Wouters, 2007. "Shocks and Frictions in US Business Cycles : a Bayesian DSGE Approach," Working Paper Research 109, National Bank of Belgium.
  2. 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.
  3. Taylor, John B, 1979. "Estimation and Control of a Macroeconomic Model with Rational Expectations," Econometrica, Econometric Society, vol. 47(5), pages 1267-86, September.
  4. Barro, Robert J., 1990. "Government Spending in a Simple Model of Endogeneous Growth," Scholarly Articles 3451296, Harvard University Department of Economics.
  5. repec:fth:harver:1473 is not listed on IDEAS
  6. Zellner, Arnold & Chen, Bin, 2001. "Bayesian Modeling Of Economies And Data Requirements," Macroeconomic Dynamics, Cambridge University Press, vol. 5(05), pages 673-700, November.
  7. 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.
  8. 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.
  9. Zellner, Arnold & Tobias, Justin, 2004. "A Note on Aggregation, Disaggregation and Forecasting Performance," Staff General Research Papers 12371, Iowa State University, Department of Economics.
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