IDEAS home Printed from https://ideas.repec.org/a/cup/macdyn/v19y2015i07p1593-1621_00.html
   My bibliography  Save this article

The Hours Worked–Productivity Puzzle: Identification In A Fractional Integration Setting

Author

Listed:
  • Lovcha, Yuliya
  • Perez-Laborda, Alejandro

Abstract

A recent finding of the SVAR literature is that the response of hours worked to a (positive) technology shock depends on the assumed order of integration of the hours. In this work we relax this assumption, allowing fractional integration in hours and productivity. We find that the sign and magnitude of the estimated responses depend crucially on the identification assumptions employed. Although the responses of hours recovered with short-run (SR) restrictions are positive in all data sets, long-run (LR) identification results in negative, although sometimes not significant responses. We check the validity of these assumptions with the Sims procedure, concluding that both LR and SR are appropriate to recover responses in a fractionally integrated VAR. However, the application of the LR scheme always results in an increase in sampling uncertainty. Results also show that even the negative responses found in the data could still be compatible with real business cycle models.

Suggested Citation

  • Lovcha, Yuliya & Perez-Laborda, Alejandro, 2015. "The Hours Worked–Productivity Puzzle: Identification In A Fractional Integration Setting," Macroeconomic Dynamics, Cambridge University Press, vol. 19(7), pages 1593-1621, October.
  • Handle: RePEc:cup:macdyn:v:19:y:2015:i:07:p:1593-1621_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S1365100514000029/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    2. 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.
    3. Canova, Fabio & Michelacci, Claudio & López-Salido, J David, 2007. "The Labour Market Effects of Technology Shocks," CEPR Discussion Papers 6365, C.E.P.R. Discussion Papers.
    4. 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.
    5. Rolf Tschernig & Enzo Weber & Roland Weigand, 2013. "Long-Run Identification in a Fractionally Integrated System," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 438-450, October.
    6. Hosoya, Yuzo, 1996. "The quasi-likelihood approach to statistical inference on multiple time-series with long-range dependence," Journal of Econometrics, Elsevier, vol. 73(1), pages 217-236, July.
    7. Gil-Alana, Luis Alberiko & Moreno, Antonio, 2009. "Technology Shocks And Hours Worked: A Fractional Integration Perspective," Macroeconomic Dynamics, Cambridge University Press, vol. 13(5), pages 580-604, November.
    8. 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.
    9. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    10. Harald Uhlig, 2004. "Do Technology Shocks Lead to a Fall in Total Hours Worked?," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 361-371, 04/05.
    11. Christopher A. Sims, 1989. "Models and Their Uses," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 489-494.
    12. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters, in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106, National Bureau of Economic Research, Inc.
    13. Jeremy Berkowitz & Francis X. Diebold, 1998. "Bootstrapping Multivariate Spectra," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 664-666, November.
    14. Faust, Jon & Leeper, Eric M, 1997. "When Do Long-Run Identifying Restrictions Give Reliable Results?," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 345-353, July.
    15. Ramey, Valerie A & Francis, Neville, 2002. "Is The Technology-Driven Real Business Cycle Hypothesis Dead? Shocks and Aggregate Fluctuations Revisted," University of California at San Diego, Economics Working Paper Series qt6x80k3nx, Department of Economics, UC San Diego.
    16. Francis, Neville & Ramey, Valerie A., 2005. "Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations revisited," Journal of Monetary Economics, Elsevier, vol. 52(8), pages 1379-1399, November.
    17. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2003. "What Happens After a Technology Shock?," NBER Working Papers 9819, National Bureau of Economic Research, Inc.
    18. Ray, Bonnie K., 1993. "Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model," International Journal of Forecasting, Elsevier, vol. 9(2), pages 255-269, August.
    19. Uwe Hassler, 1994. "(Mis)Specification Of Long Memory In Seasonal Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(1), pages 19-30, January.
    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. Lovcha, Yuliya & Pérez Laborda, Àlex, 2013. "A fractionally integrated approach to monetary policy and inflation dynamics," Working Papers 2072/211795, Universitat Rovira i Virgili, Department of Economics.
    2. Ross Doppelt & Keith O'Hara, 2018. "Bayesian Estimation of Fractionally Integrated Vector Autoregressions and an Application to Identified Technology Shocks," 2018 Meeting Papers 1212, Society for Economic Dynamics.
    3. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2018. "Monetary policy shocks, inflation persistence, and long memory," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 117-127.
    4. Yuliya Lovcha & Alejandro Perez-Laborda, 2017. "Structural shocks and dynamic elasticities in a long memory model of the US gasoline retail market," Empirical Economics, Springer, vol. 53(2), pages 405-422, September.
    5. Lovcha, Yuliya & Pérez Laborda, Alejandro, 2016. "Frequency-Domain Estimation as an Alternative to Pre-Filtering External Cycles in Structural VAR Analysis," Working Papers 2072/290743, Universitat Rovira i Virgili, Department of Economics.
    6. Lovcha, Yuliya & Pérez Laborda, Àlex, 2016. "The Variance-Frequency Decomposition as an Instrument for VAR Identification: an Application to Technology Shocks," Working Papers 2072/261537, Universitat Rovira i Virgili, Department of Economics.
    7. Lovcha, Yuliya & Pérez Laborda, Àlex, 2018. "Volatility Spillovers in a Long-Memory VAR: an Application to Energy Futures Returns," Working Papers 2072/307362, Universitat Rovira i Virgili, Department of Economics.

    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. 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.
    2. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    3. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    4. 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.
    5. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.
    6. 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.
    7. Patrick Fève & Alain Guay, 2009. "The Response of Hours to a Technology Shock: A Two‐Step Structural VAR Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(5), pages 987-1013, August.
    8. Fabrice Collard & Patrick Fève, 2012. "Sur les causes et les effets en macro économie : les Contributions de Sargent et Sims, Prix Nobel d'Economie 2011," Revue d'économie politique, Dalloz, vol. 122(3), pages 335-364.
    9. 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.
    10. Bertinelli, Luisito & Cardi, Olivier & Restout, Romain, 2022. "Labor market effects of technology shocks biased toward the traded sector," Journal of International Economics, Elsevier, vol. 138(C).
    11. Nikolay Gospodinov & Alex Maynard & Elena Pesavento, 2011. "Sensitivity of Impulse Responses to Small Low-Frequency Comovements: Reconciling the Evidence on the Effects of Technology Shocks," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(4), pages 455-467, October.
    12. 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.
    13. Olivier Cardi & Romain Restout, 2023. "Why Hours Worked Decline Less after Technology Shocks?Â," Working Papers 396800288, Lancaster University Management School, Economics Department.
    14. Gubler, Matthias & Hertweck, Matthias S., 2013. "Commodity price shocks and the business cycle: Structural evidence for the U.S," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 324-352.
    15. Laura Bisio & Andrea Faccini, 2010. "Does Cointegration Matter? An Analysis in a RBC Perspective," Working Papers in Public Economics 133, Department of Economics and Law, Sapienza University of Roma.
    16. Fabio Canova & David López-Salido & Claudio Michelacci, 2006. "On the robust effects of technology shocks on hours worked and output," Economics Working Papers 1013, Department of Economics and Business, Universitat Pompeu Fabra, revised Feb 2008.
    17. Rodolfo Mendez-Marcano, 2014. "Technology, Employment, and the Oil-Countries Business Cycle," Working Papers 1405, BBVA Bank, Economic Research Department.
    18. 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.
    19. Rujin, Svetlana, 2024. "Labor market institutions and technology-induced labor adjustment along the extensive and intensive margins," Journal of Macroeconomics, Elsevier, vol. 79(C).
    20. Neville Francis & Valerie A. Ramey, 2009. "Measures of per Capita Hours and Their Implications for the Technology‐Hours Debate," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(6), pages 1071-1097, September.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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:cup:macdyn:v:19:y:2015:i:07:p:1593-1621_00. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/mdy .

    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.