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Sensitivity of Impulse Responses to Small Low-Frequency Comovements: Reconciling the Evidence on the Effects of Technology Shocks

Author

Listed:
  • Nikolay Gospodinov
  • Alex Maynard
  • Elena Pesavento

Abstract

This article clarifies the empirical source of the debate on the effect of technology shocks on hours worked. We find that the contrasting conclusions from levels and differenced vector autoregression specifications, documented in the literature, can be explained by a small low-frequency comovement between hours worked and productivity growth that gives rise to a discontinuity in the solution for the structural coefficients identified by long-run restrictions. Whereas the low-frequency comovement is allowed for in the levels specification, it is implicitly set to 0 in the differenced vector autoregression. Consequently, even when the root of hours is very close to 1 and the low-frequency comovement is quite small, removing it can give rise to biases of sufficient size to account for the empirical difference between the two specifications.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:jnlbes:v:29:y:2011:i:4:p:455-467
    DOI: 10.1198/jbes.2011.10042
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    Cited by:

    1. Michelle Alexopoulos & Jon Cohen, 2012. "The Effects of Computer Technologies on the Canadian Economy: Evidence from New Direct Measures," International Productivity Monitor, Centre for the Study of Living Standards, vol. 23, pages 17-32, Spring.
    2. 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.
    3. 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.
    4. 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.
    5. Alexopoulos, Michelle & Tombe, Trevor, 2012. "Management matters," Journal of Monetary Economics, Elsevier, vol. 59(3), pages 269-285.
    6. Lutz Kilian, 2013. "Structural vector autoregressions," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 22, pages 515-554, Edward Elgar Publishing.
    7. Jinho Choi & Juan Carlos Escanciano & Junjie Guo, 2022. "Generalized band spectrum estimation with an application to the New Keynesian Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1055-1078, August.
    8. Kurt Graden Lunsford, 2023. "Business Cycles and Low-Frequency Fluctuations in the US Unemployment Rate," Working Papers 23-19, Federal Reserve Bank of Cleveland.
    9. Nadav Ben Zeev, 2019. "Is There A Single Shock That Drives The Majority Of Business Cycle Fluctuations?," Working Papers 1906, Ben-Gurion University of the Negev, Department of Economics.
    10. Yohei Yamamoto & Pierre Perron, 2013. "Estimating and testing multiple structural changes in linear models using band spectral regressions," Econometrics Journal, Royal Economic Society, vol. 16(3), pages 400-429, October.
    11. Chaudourne, Jeremy & Fève, Patrick & Guay, Alain, 2014. "Understanding the effect of technology shocks in SVARs with long-run restrictions," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 154-172.
    12. Lenard Lieb & Stephan Smeekes, 2017. "Inference for Impulse Responses under Model Uncertainty," Papers 1709.09583, arXiv.org, revised Oct 2019.
    13. 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.
    14. Michelle Alexopoulos & Jon Cohen, 2016. "The Medium Is the Measure: Technical Change and Employment, 1909—1949," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 792-810, October.
    15. Riccardo DiCecio & Michael T. Owyang, 2010. "Identifying technology shocks in the frequency domain," Working Papers 2010-025, Federal Reserve Bank of St. Louis.
    16. Nikolay Gospodinov & Damba Lkhagvasuren, 2014. "A Moment‐Matching Method For Approximating Vector Autoregressive Processes By Finite‐State Markov Chains," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 843-859, August.

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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