IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/201207.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.up.ac.za/media/shared/61/WP/wp264.zp39560.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. repec:fth:harver:1473 is not listed on IDEAS
    2. Taylor, John B, 1979. "Estimation and Control of a Macroeconomic Model with Rational Expectations," Econometrica, Econometric Society, vol. 47(5), pages 1267-1286, September.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Zellner, Arnold & Israilevich, Guillermo, 2005. "Marshallian Macroeconomic Model: A Progress Report," Macroeconomic Dynamics, Cambridge University Press, vol. 9(2), pages 220-243, April.
    8. 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.
    9. 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.
    10. 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.
    11. Zellner, Arnold & Chen, Bin, 2001. "Bayesian Modeling Of Economies And Data Requirements," Macroeconomic Dynamics, Cambridge University Press, vol. 5(5), pages 673-700, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arnold Zellner & Jacques Kibambe Ngoie, 2015. "Evaluation of the Effects of Reduced Personal and Corporate Tax Rates on the Growth Rates of the U.S. Economy," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 56-81, February.
    2. Kim, Kun Ho, 2011. "Density forecasting through disaggregation," International Journal of Forecasting, Elsevier, vol. 27(2), pages 394-412.
    3. Kim, Kun Ho, 2011. "Density forecasting through disaggregation," International Journal of Forecasting, Elsevier, vol. 27(2), pages 394-412, April.
    4. Ngoie, Jacques Kibambe & Zellner, Arnold, 2012. "The Use Of A Marshallian Macroeconomic Model For Policy Evaluation: Case Of South Africa," Macroeconomic Dynamics, Cambridge University Press, vol. 16(3), pages 423-448, June.
    5. Banerjee, Sanjibani & A. Barnett, William & A. Duzhak, Evgeniya & Gopalan, Ramu, 2011. "Bifurcation analysis of Zellner's Marshallian Macroeconomic Model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(9), pages 1577-1585, September.
    6. Etro, Federico, 2017. "Research in economics and macroeconomics," Research in Economics, Elsevier, vol. 71(3), pages 373-383.
    7. Zellner, Arnold & Israilevich, Guillermo, 2005. "The Marshallian macroeconomic model: A progress report," International Journal of Forecasting, Elsevier, vol. 21(4), pages 627-645.
    8. Ricardo Nunes & Jinill Kim & Jesper Linde & Davide Debortoli, 2014. "Designing a Simple Loss Function for the Fed: Does the Dual Mandate Make Sense?," 2014 Meeting Papers 1043, Society for Economic Dynamics.
    9. Prabheesh, K.P. & Anglingkusumo, Reza & Juhro, Solikin M., 2021. "The dynamics of global financial cycle and domestic economic cycles: Evidence from India and Indonesia," Economic Modelling, Elsevier, vol. 94(C), pages 831-842.
    10. Danilo Cascaldi-Garcia & Marija Vukotic, 2022. "Patent-Based News Shocks," The Review of Economics and Statistics, MIT Press, vol. 104(1), pages 51-66, March.
    11. Barrales-Ruiz, Jose & Arnim, Rudiger von, 2021. "Endogenous fluctuations in demand and distribution: An empirical investigation," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 204-220.
    12. Lubik, Thomas A. & Matthes, Christian & Verona, Fabio, 2019. "Assessing U.S. aggregate fluctuations across time and frequencies," Bank of Finland Research Discussion Papers 5/2019, Bank of Finland.
    13. Gallegati, Marco & Giri, Federico & Palestrini, Antonio, 2019. "DSGE model with financial frictions over subsets of business cycle frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 152-163.
    14. Jordi Galí & Mark Gertler, 2007. "Macroeconomic Modeling for Monetary Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 21(4), pages 25-46, Fall.
    15. Laséen, Stefan & Pescatori, Andrea & Turunen, Jarkko, 2017. "Systemic risk: A new trade-off for monetary policy?," Journal of Financial Stability, Elsevier, vol. 32(C), pages 70-85.
    16. Martin Larch & João Nogueira Martins, 2007. "Fiscal indicators - Proceedings of the the Directorate-General for Economic and Financial Affairs Workshop held on 22 September 2006 in Brussels," European Economy - Economic Papers 2008 - 2015 297, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    17. DJINKPO, Medard, 2019. "A DSGE model for Fiscal Policy Analysis in The Gambia," MPRA Paper 97874, University Library of Munich, Germany, revised 30 Dec 2019.
    18. Laureys, Lien & Meeks, Roland & Wanengkirtyo, Boromeus, 2021. "Optimal simple objectives for monetary policy when banks matter," European Economic Review, Elsevier, vol. 135(C).
    19. Bechný Jakub, 2019. "Output gap in the Czech economy: DSGE approach," Review of Economic Perspectives, Sciendo, vol. 19(2), pages 137-156, June.
    20. Klug, Thorsten & Mayer, Eric & Schuler, Tobias, 2022. "The corporate saving glut and the current account in Germany," Journal of International Money and Finance, Elsevier, vol. 121(C).

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pre:wpaper:201207. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.