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Understanding the Effect of Technology Shocks in SVARs with Long-Run Restrictions

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  • Chaudourne, Jeremy
  • Fève, Patrick
  • Guay, Alain

Abstract

This paper studies the statistical properties of impulse response functions in structural vector autoregressions (SVARs) with a highly persistent variable as hours worked and long-run identifying restrictions. The highly persistent variable is specified as a nearly stationary persistent process. Such process appears particularly well suited to characterized the dynamics of hours worked because it implies a unit root in finite sample but is asymptotically stationary and persistent. This is typically the case for per capita hours worked which are included in SVARs. Theoretical results derived from this specification allow to explain most of the empirical findings from SVARs which include U.S. hours worked. Simulation experiments from an estimated DSGE model confirm theoretical results.

Suggested Citation

  • Chaudourne, Jeremy & Fève, Patrick & Guay, Alain, 2012. "Understanding the Effect of Technology Shocks in SVARs with Long-Run Restrictions," IDEI Working Papers 738, Institut d'Économie Industrielle (IDEI), Toulouse.
    • Handle: RePEc:ide:wpaper:26113
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    Cited by:

    1. Ivashchenko, S., 2020. "Long-term growth sources for sectors of Russian economy," Journal of the New Economic Association, New Economic Association, vol. 48(4), pages 86-112.
    2. Benchimol, Jonathan & Ivashchenko, Sergey, 2021. "Switching volatility in a nonlinear open economy," Journal of International Money and Finance, Elsevier, vol. 110(C).
    3. Sergey Ivashchenko, 2022. "Dynamic Stochastic General Equilibrium Model with Multiple Trends and Structural Breaks," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 46-72, March.
    4. 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).
    5. Mathew Ekundayo Rotimi & Harold Ngalawa, 2017. "Oil Price Shocks and Economic Performance in Africa’s Oil Exporting Countries," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 13(5), pages 169-188, OCTOBER.
    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. Thomet, Jacqueline & Wegmueller, Philipp, 2021. "Technology Shocks And Hours Worked: A Cross-Country Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 25(4), pages 1020-1052, June.
    8. 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.
    9. 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.
    10. Sergey M. Ivashchenko, 2019. "DSGE Models: Problem of Trends," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 2, pages 81-95, April.

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    More about this item

    Keywords

    SVARs; long-run restrictions; locally nonstationary process; technology shocks; hours worked;
    All these keywords.

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

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