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Modeling and policy analysis for the U.S. Science Sector

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  • Jacques Kibambe Ngoie
  • Arnold Zellner

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 Economic Research Southern Africa in its series Working Papers with number 264.

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Length: 21 pages
Date of creation: 2012
Date of revision:
Handle: RePEc:rza:wpaper:264

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

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  1. Barro, Robert J., 1990. "Government Spending in a Simple Model of Endogeneous Growth," Scholarly Articles 3451296, Harvard University Department of Economics.
  2. Tobias, Justin & Zellner, Arnold, 2000. "A Note on Aggregation, Disaggregation and Forecasting Performance," Staff General Research Papers 12024, Iowa State University, Department of Economics.
  3. Smets, Frank & Wouters, Raf, 2007. "Shocks and frictions in US business cycles: a Bayesian DSGE approach," Working Paper Series, European Central Bank 0722, European Central Bank.
  4. Zellner, Arnold & Chen, Bin, 2001. "Bayesian Modeling Of Economies And Data Requirements," Macroeconomic Dynamics, Cambridge University Press, Cambridge University Press, vol. 5(05), pages 673-700, November.
  5. Marianne Baxter & Robert G. King, 1995. "Measuring Business Cycles Approximate Band-Pass Filters for Economic Time Series," NBER Working Papers 5022, National Bureau of Economic Research, Inc.
  6. Zvi Griliches, 1990. "Patent Statistics as Economic Indicators: A Survey," NBER Working Papers 3301, National Bureau of Economic Research, Inc.
  7. Veloce, William & Zellner, Arnold, 1985. "Entry and empirical demand and supply analysis for competitive industries," Journal of Econometrics, Elsevier, Elsevier, vol. 30(1-2), pages 459-471.
  8. repec:fth:harver:1473 is not listed on IDEAS
  9. Taylor, John B, 1979. "Estimation and Control of a Macroeconomic Model with Rational Expectations," Econometrica, Econometric Society, Econometric Society, vol. 47(5), pages 1267-86, September.
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