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R&D Growth and Business Cycles Measured with an Endogenous Growth DSGE Model

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  • Hasumi, Ryo
  • Iiboshi, Hirokuni
  • Nakamura, Daisuke

Abstract

We consider how and the extent to which a pure technology shock driven by R&D activities impacts on business cycles as well as economic growth, using a medium-scale neo-classical dynamic stochastic general equilibrium (DSGE) model following Comin and Gertler (2006). We try to identify a pure technology shock by adopting "intellectual property product" first entered in 2008 SNA which can be regarded as R&D activity, and by assuming "time to build" by Kydland and Prescott (1982) in the process converting from innovations to products. Our empirical result based on a Bayesian analysis reports a common stochastic trend driven by the pure technology shock is likely to be procyclical, and it accounts for nearly half of variation of the real GDP whose remaining is explained by business cycle components. Meanwhile, a TFP shock, substituting for the R&D shocks, seems to move the common trend independently with business cycle.

Suggested Citation

  • Hasumi, Ryo & Iiboshi, Hirokuni & Nakamura, Daisuke, 2017. "R&D Growth and Business Cycles Measured with an Endogenous Growth DSGE Model," MPRA Paper 85525, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:85525
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    References listed on IDEAS

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    1. Miles S. Kimball & John G. Fernald & Susanto Basu, 2006. "Are Technology Improvements Contractionary?," American Economic Review, American Economic Association, vol. 96(5), pages 1418-1448, December.
    2. Daisuke Ikeda & Takushi Kurozumi, 2019. "Slow Post-financial Crisis Recovery and Monetary Policy," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(4), pages 82-112, October.
    3. Fumio Hayashi & Edward C. Prescott, 2004. "The 1990s in Japan: a lost decade," Chapters, in: Paolo Onofri (ed.), The Economics of an Ageing Population, chapter 2, Edward Elgar Publishing.
    4. Adolfson, Malin & Laseen, Stefan & Linde, Jesper & Villani, Mattias, 2007. "Bayesian estimation of an open economy DSGE model with incomplete pass-through," Journal of International Economics, Elsevier, vol. 72(2), pages 481-511, July.
    5. David Altig & Lawrence Christiano & Martin Eichenbaum & Jesper Linde, 2011. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(2), pages 225-247, April.
    6. Diego Comin & Mark Gertler, 2006. "Medium-Term Business Cycles," American Economic Review, American Economic Association, vol. 96(3), pages 523-551, June.
    7. Pablo A. Guerron‐Quintana & Ryo Jinnai, 2019. "Financial frictions, trends, and the great recession," Quantitative Economics, Econometric Society, vol. 10(2), pages 735-773, May.
    8. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    9. Yasuo Hirose & Atsushi Inoue, 2016. "The Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 630-651, June.
    10. Michael D. Bordo & Joseph G. Haubrich, 2017. "Deep Recessions, Fast Recoveries, And Financial Crises: Evidence From The American Record," Economic Inquiry, Western Economic Association International, vol. 55(1), pages 527-541, January.
    11. 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.
    12. David Altig & Lawrence Christiano & Martin Eichenbaum & Jesper Linde, 2011. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(2), pages 225-247, April.
    13. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    14. Michelle Alexopoulos, 2011. "Read All about It!! What Happens Following a Technology Shock?," American Economic Review, American Economic Association, vol. 101(4), pages 1144-1179, June.
    15. Romer, Paul M, 1990. "Endogenous Technological Change," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 71-102, October.
    16. Howard Kung & Lukas Schmid, 2015. "Innovation, Growth, and Asset Prices," Journal of Finance, American Finance Association, vol. 70(3), pages 1001-1037, June.
    17. Pablo A. Guerron-Quintana & Tomohiro Hirano & Ryo Jinnai, 2018. "Recurrent Bubbles, Economic Fluctuations, and Growth," Bank of Japan Working Paper Series 18-E-5, Bank of Japan.
    18. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
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    More about this item

    Keywords

    R&D shock; technology shock; dynamic stochastic general equilibrium model; common stochastic trend; endogenous growth model;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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