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Do Technology Shocks Drive Hours Up Or Down? A Little Evidence From An Agnostic Procedure

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  • PESAVENTO, ELENA
  • ROSSI, BARBARA

Abstract

This paper analyzes the robustness of the estimate of a positive productivity shock on hours to the presence of a possible unit root in hours. Estimations in levels or in first differences provide opposite conclusions. We rely on an agnostic procedure in which the researcher does not have to choose between a specification in levels or in first differences. We find that a positive productivity shock has a negative impact effect on hours, but the effect is much shorter lived, and disappears after two quarters. The effect becomes positive at business-cycle frequencies, although it is not significant.

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  • Pesavento, Elena & Rossi, Barbara, 2005. "Do Technology Shocks Drive Hours Up Or Down? A Little Evidence From An Agnostic Procedure," Macroeconomic Dynamics, Cambridge University Press, vol. 9(4), pages 478-488, September.
    • Handle: RePEc:cup:macdyn:v:9:y:2005:i:04:p:478-488_04
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    1. Elliott, Graham & Jansson, Michael, 2003. "Testing for unit roots with stationary covariates," Journal of Econometrics, Elsevier, vol. 115(1), pages 75-89, July.
    2. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
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    5. Graham Elliott & Michael Jansson & Elena Pesavento, 2005. "Optimal Power for Testing Potential Cointegrating Vectors With Known Parameters for Nonstationarity," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 34-48, January.
    6. Elliott, Graham & Stock, James H., 2001. "Confidence intervals for autoregressive coefficients near one," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 155-181, July.
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    Cited by:

    1. 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.
    2. 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.
    3. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    4. Elena Pesavento & Barbara Rossi, 2006. "Small‐sample confidence intervals for multivariate impulse response functions at long horizons," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1135-1155, December.
    5. Ulrich K. Müller & Mark W. Watson, 2008. "Testing Models of Low-Frequency Variability," Econometrica, Econometric Society, vol. 76(5), pages 979-1016, September.
    6. Chevillon, Guillaume & Mavroeidis, Sophocles & Zhan, Zhaoguo, 2016. "Robust inference in structural VARs with long-run restrictions," ESSEC Working Papers WP1702, ESSEC Research Center, ESSEC Business School.
    7. Ghent, Andra C., 2009. "Comparing DSGE-VAR forecasting models: How big are the differences?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 864-882, April.
    8. Christoph Gortz & Christopher Gunn & Thomas A. Lubik, 2024. "The Changing Nature of Technology Shocks," Working Paper 99119, Federal Reserve Bank of Richmond.
    9. Ali YOUSEFI & Sadegh KHALILIAN & Mohammad Hadi HAJIAN, 2010. "The Role of Water Sector in Iranian Economy: A CGE Modeling Approach," EcoMod2010 259600173, EcoMod.
    10. Cristiano Cantore & Miguel León-Ledesma & Peter McAdam & Alpo Willman, 2014. "Shocking Stuff: Technology, Hours, And Factor Substitution," Journal of the European Economic Association, European Economic Association, vol. 12(1), pages 108-128, February.
    11. 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.
    12. Morten O. Ravn & Saverio Simonelli, 2007. "Labor Market Dynamics and the Business Cycle: Structural Evidence for the United States," Scandinavian Journal of Economics, Wiley Blackwell, vol. 109(4), pages 743-777, December.
    13. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2014. "Persistence and cycles in US hours worked," Economic Modelling, Elsevier, vol. 38(C), pages 504-511.
    14. Rujin, Svetlana, 2024. "Labor market institutions and technology-induced labor adjustment along the extensive and intensive margins," Journal of Macroeconomics, Elsevier, vol. 79(C).
    15. 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.
    16. Rujin, Svetlana, 2019. "What are the effects of technology shocks on international labor markets?," Ruhr Economic Papers 806, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    17. Ghent, Andra, 2006. "Comparing Models of Macroeconomic Fluctuations: How Big Are the Differences?," MPRA Paper 180, University Library of Munich, Germany.
    18. Christoph Gortz & Christopher Gunn & Thomas Lubik, 2022. "Split Personalities: The Changing Nature of Technology Shocks," Carleton Economic Papers 22-06, Carleton University, Department of Economics.
    19. Di Pace, Federico & Villa, Stefania, 2016. "Factor complementarity and labour market dynamics," European Economic Review, Elsevier, vol. 82(C), pages 70-112.
    20. Beate Schirwitz, 2013. "Business Fluctuations, Job Flows and Trade Unions - Dynamics in the Economy," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 47, September.
    21. 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.
    22. Riccardo DiCecio & Michael T. Owyang, 2010. "Identifying technology shocks in the frequency domain," Working Papers 2010-025, Federal Reserve Bank of St. Louis.
    23. Hutter, Christian & Weber, Enzo, 2021. "Labour market miracle, productivity debacle: Measuring the effects of skill-biased and skill-neutral technical change," Economic Modelling, Elsevier, vol. 102(C).

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    More about this item

    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
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • F40 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - General

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