IDEAS home Printed from https://ideas.repec.org/p/ide/wpaper/4482.html
   My bibliography  Save this paper

Technology Shocks and Employment: Do We Really Need DSGE Models with a Fall in Hours?

Author

Listed:
  • Dupaigne, Martial
  • Fève, Patrick
  • Matheron, Julien

Abstract

The recent empirical literature that uses Structural Vector Autoregressions (SVAR) has shown that productivity shocks identified using long--run restrictions lead to a persistent and significant decline in hours worked. This evidence calls into question standard RBC models in which a positive technology shock leads to a rise in hours. In this paper, we estimate and test a standard RBC model using Indirect Inference on impulse responses of hours worked after technology and non-technology shocks. We find that this model is not rejected by the data and is able to produce impulse responses in SVAR from simulated data similar to impulse responses in SVAR from actual data. Moreover, technology shocks represent the main contribution to the variance of the business cycle component of output under the estimated DSGE model. Our results suggest that we do not necessarily need DSGE models with a fall in hours to reproduce the results deriving from SVAR models.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Dupaigne, Martial & Fève, Patrick & Matheron, Julien, 2005. "Technology Shocks and Employment: Do We Really Need DSGE Models with a Fall in Hours?," IDEI Working Papers 349, Institut d'Économie Industrielle (IDEI), Toulouse.
    • Handle: RePEc:ide:wpaper:4482
    as

    Download full text from publisher

    File URL: http://idei.fr/sites/default/files/medias/doc/wp/2005/technology_shocks.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Prescott, Edward C., 1986. "Theory ahead of business-cycle measurement," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 25(1), pages 11-44, January.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Hansen, Gary D., 1985. "Indivisible labor and the business cycle," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 309-327, November.
    4. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    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. Hall, Robert E, 1997. "Macroeconomic Fluctuations and the Allocation of Time," Journal of Labor Economics, University of Chicago Press, vol. 15(1), pages 223-250, January.
    7. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    8. Lawrence J. Christiano & Michele Boldrin & Jonas D. M. Fisher, 2001. "Habit Persistence, Asset Returns, and the Business Cycle," American Economic Review, American Economic Association, vol. 91(1), pages 149-166, March.
    9. Giannone, Domenico & Reichlin, Lucrezia & Sala, Luca, 2006. "VARs, common factors and the empirical validation of equilibrium business cycle models," Journal of Econometrics, Elsevier, vol. 132(1), pages 257-279, May.
    10. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2007. "Business Cycle Accounting," Econometrica, Econometric Society, vol. 75(3), pages 781-836, May.
    11. Neville Francis & Valerie A. Ramey, 2006. "The Source of Historical Economic Fluctuations: An Analysis Using Long-Run Restrictions," NBER Chapters, in: NBER International Seminar on Macroeconomics 2004, pages 17-73, National Bureau of Economic Research, Inc.
    12. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
    13. Martin S. Eichenbaum & Lars Peter Hansen & Kenneth J. Singleton, 1988. "A Time Series Analysis of Representative Agent Models of Consumption and Leisure Choice Under Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 103(1), pages 51-78.
    14. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
    15. Anderson, Gary & Moore, George, 1985. "A linear algebraic procedure for solving linear perfect foresight models," Economics Letters, Elsevier, vol. 17(3), pages 247-252.
    16. Galí, Jordi & Rabanal, Pau, 2004. "Technology Shocks and Aggregate Fluctuations: How Well Does the RBC Model Fit Post-War US Data?," CEPR Discussion Papers 4522, C.E.P.R. Discussion Papers.
    17. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
    18. Gourieroux, C & Monfort, A & Renault, E, 1993. "Indirect Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 85-118, Suppl. De.
    19. Jordi Galí, 2004. "On The Role of Technology Shocks as a Source of Business Cycles: Some New Evidence," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 372-380, 04/05.
    20. Neville Francis & Valerie A. Ramey, 2002. "Is the Technology-Driven Real Business Cycle Hypothesis Dead?," NBER Working Papers 8726, National Bureau of Economic Research, Inc.
    21. Julio J. Rotemberg & Michael Woodford, 1997. "An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy," NBER Chapters, in: NBER Macroeconomics Annual 1997, Volume 12, pages 297-361, National Bureau of Economic Research, Inc.
    22. Bover, Olympia, 1991. "Relaxing Intertemporal Separability: A Rational Habits Model of Labor Supply Estimated from Panel Data," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 85-100, January.
    23. Beaudry, Paul & Guay, Alain, 1996. "What do interest rates reveal about the functioning of real business cycle models?," Journal of Economic Dynamics and Control, Elsevier, vol. 20(9-10), pages 1661-1682.
    24. 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.
    25. Hansen, Gary D., 1997. "Technical progress and aggregate fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 21(6), pages 1005-1023, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Patrick Fève, 2005. "Voies de la modélisation macro-économétrique?," Revue Française d'Économie, Programme National Persée, vol. 20(1), pages 147-179.
    2. Mvondo, Thierry, 2021. "Stabilisation et relance macroéconomiques post COVID-19 dans la CEMAC : Quels instruments pour quels effets dans un modèle DSGE ?," Dynare Working Papers 65, CEPREMAP.
    3. Sanvi Avouyi‐Dovi & Julien Matheron, 2007. "Technology Shocks and Monetary Policy: Revisiting the Fed's Performance," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2‐3), pages 471-507, March.
    4. Martial Dupaigne & Patrick Feve, 2009. "Technology shocks around the world," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(4), pages 592-607, October.
    5. Sevgi Coskun, 2020. "Technology Shocks and Non-stationary Hours in Emerging Countries and DSVAR," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 14(2), pages 129-163, May.
    6. repec:dau:papers:123456789/5491 is not listed 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. Martial Dupaigne & Patrick Feve & Julien Matheron, 2007. "Technology Shocks, Non-stationary Hours and DSVAR," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(2), pages 238-255, April.
    2. Andrei Polbin & Sergey Drobyshevsky, 2014. "Developing a Dynamic Stochastic Model of General Equilibrium for the Russian Economy," Research Paper Series, Gaidar Institute for Economic Policy, issue 166P, pages 156-156.
    3. Chari, V.V. & Kehoe, Patrick J. & McGrattan, Ellen R., 2008. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1337-1352, November.
    4. 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.
    5. Sergio Rebelo, 2005. "Real Business Cycle Models: Past, Present, and Future," NBER Working Papers 11401, National Bureau of Economic Research, Inc.
    6. Charles L. Evans & David A. Marshall, 2009. "Fundamental Economic Shocks and the Macroeconomy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(8), pages 1515-1555, December.
    7. Jordi Gali & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations: How Well Does the RBS Model Fit Postwar U.S. Data?," NBER Working Papers 10636, National Bureau of Economic Research, Inc.
    8. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2004. "A Critique of Structural VARs Using Real Business Cycle Theory," Levine's Bibliography 122247000000000518, UCLA Department of Economics.
    9. Jordi Gali, 2005. "Trends in hours, balanced growth, and the role of technology in the business cycle," Review, Federal Reserve Bank of St. Louis, vol. 87(Jul), pages 459-486.
    10. Elmar Mertens, 2005. "Puzzling Comovements between Output and Interest Rates? Multiple Shocks are the Answer," Working Papers 05.05, Swiss National Bank, Study Center Gerzensee.
    11. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    12. Mertens, Elmar, 2010. "Structural shocks and the comovements between output and interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1171-1186, June.
    13. Riccardo DiCecio, 2004. "Comovement: it's not a puzzle," 2004 Meeting Papers 113, Society for Economic Dynamics.
    14. DiCecio, Riccardo, 2009. "Sticky wages and sectoral labor comovement," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 538-553, March.
    15. Peter Ireland & Scott Schuh, 2008. "Productivity and U.S. Macroeconomic Performance: Interpreting the Past and Predicting the Future with a Two-Sector Real Business Cycle Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 11(3), pages 473-492, July.
    16. Avouyi-Dovi, S. & Matheron, J., 2005. "Technology Shocks and Monetary Policy in an Estimated Sticky Price Model of the US Economy," Working papers 123, Banque de France.
    17. Tripier, Fabien, 2006. "Sticky prices, fair wages, and the co-movements of unemployment and labor productivity growth," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2749-2774, December.
    18. Giancarlo Corsetti & Luca Dedola & Sylvain Leduc, 2008. "Productivity, External Balance, and Exchange Rates: Evidence on the Transmission Mechanism among G7 Countries," NBER Chapters, in: NBER International Seminar on Macroeconomics 2006, pages 117-194, National Bureau of Economic Research, Inc.
    19. Justiniano, Alejandro & Primiceri, Giorgio E. & Tambalotti, Andrea, 2010. "Investment shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 132-145, March.
    20. Fève, P. & Matheron, J., 2005. "Can the Kydland--Prescott Model Pass the Cogley--Nason Test?," Working papers 125, Banque de France.

    More about this item

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:ide:wpaper:4482. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/idtlsfr.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.