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Technology Shocks and Hours Worked: A Fractional Integration Perspective

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  • Luis Alberiko Gil-Alana

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  • Antonio Moreno

    ()

Abstract

Previous research has found that the response of hours worked to a technology shock crucially depends on whether the variable hours is assumed to be an I(0) or an I(1) variable ex-ante. In this paper we employ a multivariate fractionally integrated model which allows us to determine simultaneously the order of integration of hours worked and the response of hours to a technology shock. We find that hours fall on impact in response to a positive technology shock.

Suggested Citation

  • Luis Alberiko Gil-Alana & Antonio Moreno, 2006. "Technology Shocks and Hours Worked: A Fractional Integration Perspective," Faculty Working Papers 03/06, School of Economics and Business Administration, University of Navarra.
  • Handle: RePEc:una:unccee:wp0306
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Nadia Ayari & Szabolcs Blazsek & Pedro Mendi, 2012. "Renewable energy innovations in Europe: a dynamic panel data approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3135-3147, August.
    2. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2014. "Persistence and cycles in US hours worked," Economic Modelling, Elsevier, vol. 38(C), pages 504-511.
    3. Luis Gil-Alana & Antonio Moreno, 2012. "Fractional integration and structural breaks in U.S. macro dynamics," Empirical Economics, Springer, vol. 43(1), pages 427-446, August.
    4. Lovcha, Yuliya & Pérez Laborda, Àlex, 2013. "Hours worked - Productivity puzzle: identification in fractional integration settings," Working Papers 2072/211796, Universitat Rovira i Virgili, Department of Economics.
    5. Tschernig, Rolf & Weber, Enzo & Weigand, Roland, 2014. "Long- versus medium-run identification in fractionally integrated VAR models," Economics Letters, Elsevier, vol. 122(2), pages 299-302.
    6. Ibrahim ARISOY, 2012. "Structural breaks and nonlinearities in hours worked: are they really nonstationary?," Economics Bulletin, AccessEcon, vol. 32(3), pages 2670-2677.
    7. Blazsek, Szabolcs & Escribano, Álvaro, 2012. "Patents, secret innovations and firm's rate of return : differential effects of the innovation leader," UC3M Working papers. Economics we1202, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Gianluca Moretti & Giulio Nicoletti, 2010. "Estimating DSGE models with unknown data persistence," Temi di discussione (Economic working papers) 750, Bank of Italy, Economic Research and International Relations Area.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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