IDEAS home Printed from https://ideas.repec.org/p/mcm/deptwp/2016-11.html
   My bibliography  Save this paper

Learning Efficiency Shocks, Knowledge Capital and the Business Cycle: A Bayesian Evaluation

Author

Listed:
  • Alok Johri
  • Muhebullah Karimzada

Abstract

We incorporate shocks to the efficiency with which firms learn from production activity and accumulate knowledge into an otherwise standard real DSGE model with imperfect competition. Using real aggregate data and Bayesian inference techniques, we find that learning efficiency shocks are an important source of observed variation in the growth rate of aggregate output, investment, consumption and especially hours worked in post-war US data. The estimated shock processes suggest much less exogenous variation in preferences and total factor productivity are needed by our model to account for the joint dynamics of consumption and hours. This occurs because learning efficiency shocks induce shifts in labour demand uncorrelated with current TFP, a role usually played by preference shocks. At the same time, knowledge capital acts like an endogenous source of productivity variation in the model. Measures of model fit prefer the specification with learning efficiency shocks.

Suggested Citation

  • Alok Johri & Muhebullah Karimzada, 2016. "Learning Efficiency Shocks, Knowledge Capital and the Business Cycle: A Bayesian Evaluation," Department of Economics Working Papers 2016-11, McMaster University.
  • Handle: RePEc:mcm:deptwp:2016-11
    as

    Download full text from publisher

    File URL: http://socserv.mcmaster.ca/econ/rsrch/papers/archive/McMasterEconWP2016-11.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Fabio Canova & Filippo Ferroni, 2011. "Multiple filtering devices for the estimation of cyclical DSGE models," Quantitative Economics, Econometric Society, vol. 2(1), pages 73-98, March.
    2. C. Lanier Benkard, 2000. "Learning and Forgetting: The Dynamics of Aircraft Production," American Economic Review, American Economic Association, vol. 90(4), pages 1034-1054, September.
    3. Bahk, Byong-Hong & Gort, Michael, 1993. "Decomposing Learning by Doing in New Plants," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 561-583, August.
    4. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    5. Johri, Alok & Letendre, Marc-André & Luo, Daqing, 2011. "Organizational capital and the international co-movement of investment," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 511-523.
    6. Christopher Gunn & Alok Johri, 2011. "News and knowledge capital," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 92-101, January.
    7. 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.
    8. Johri, Alok & Lahiri, Amartya, 2008. "Persistent real exchange rates," Journal of International Economics, Elsevier, vol. 76(2), pages 223-236, December.
    9. Cooper, Russell & Johri, Alok, 2002. "Learning-by-doing and aggregate fluctuations," Journal of Monetary Economics, Elsevier, vol. 49(8), pages 1539-1566, November.
    10. Andrew Atkeson & Patrick J. Kehoe, 2005. "Modeling and Measuring Organization Capital," Journal of Political Economy, University of Chicago Press, vol. 113(5), pages 1026-1053, October.
    11. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    12. Irwin, Douglas A & Klenow, Peter J, 1994. "Learning-by-Doing Spillovers in the Semiconductor Industry," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1200-1227, December.
    13. Yongsung Chang & Joao F. Gomes & Frank Schorfheide, 2002. "Learning-by-Doing as a Propagation Mechanism," American Economic Review, American Economic Association, vol. 92(5), pages 1498-1520, December.
    14. Sherwin Rosen, 1972. "Learning by Experience as Joint Production," The Quarterly Journal of Economics, Oxford University Press, vol. 86(3), pages 366-382.
    15. Clarke, Andrew J. & Johri, Alok, 2009. "Procyclical Solow Residuals Without Technology Shocks," Macroeconomic Dynamics, Cambridge University Press, vol. 13(03), pages 366-389, June.
    16. Alok Johri, 2009. "Delivering Endogenous Inertia in Prices and Output," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(4), pages 736-754, October.
    17. Johri, Alok & Letendre, Marc-Andre, 2007. "What do `residuals' from first-order conditions reveal about DGE models?," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2744-2773, August.
    18. Fabio Canova & David Lopez-Salido & Claudio Michelacci, 2010. "The effects of technology shocks on hours and output: a robustness analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 755-773.
    19. Kenneth J. Arrow, 1962. "The Economic Implications of Learning by Doing," Review of Economic Studies, Oxford University Press, vol. 29(3), pages 155-173.
    20. Prescott, Edward C & Visscher, Michael, 1980. "Organization Capital," Journal of Political Economy, University of Chicago Press, vol. 88(3), pages 446-461, June.
    21. Fernald, John G., 2007. "Trend breaks, long-run restrictions, and contractionary technology improvements," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2467-2485, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Learning Efficiency Shocks, Knowledge Capital and the Business Cycle: A Bayesian Evaluation
      by Christian Zimmermann in NEP-DGE blog on 2017-01-31 22:40:35

    More about this item

    Keywords

    Business Cycles; Learning-by-Doing; Learning Efficiency Shocks; Knowledge Capital;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:mcm:deptwp:2016-11. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://edirc.repec.org/data/demcmca.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.