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Methods versus substance: measuring the effects of technology shocks on hours

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Abstract

In this paper, we employ both calibration and modern (Bayesian) estimation methods to assess the role of neutral and investment-specific technology shocks in generating fluctuations in hours. Using a neoclassical stochastic growth model, we show how answers are shaped by the identification strategies and not by the statistical approaches. The crucial parameter is the labor supply elasticity. Both a calibration procedure that uses modern assessments of the Frisch elasticity and the estimation procedures result in technology shocks accounting for 2% to 9% of the variation in hours worked in the data. We infer that we should be talking more about identification and less about the choice of particular quantitative approaches.

Suggested Citation

  • Cristina Fuentes-Albero & Maxym Kryshko & José-Víctor Ríos-Rull & Raul Santaeulalia-Llopis & Frank Schorfheide, 2009. "Methods versus substance: measuring the effects of technology shocks on hours," Staff Report 433, Federal Reserve Bank of Minneapolis.
    • Handle: RePEc:fip:fedmsr:433
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Technology shocks and hours: it is the identification, stupid!
      by Economic Logician in Economic Logic on 2009-10-13 19:05:00

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    Cited by:

    1. Frank Schorfheide, 2011. "Estimation and Evaluation of DSGE Models: Progress and Challenges," NBER Working Papers 16781, National Bureau of Economic Research, Inc.
    2. Franke, Reiner & Jang, Tae-Seok & Sacht, Stephen, 2015. "Moment matching versus Bayesian estimation: Backward-looking behaviour in a New-Keynesian baseline model," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 126-154.
    3. Jiang, Kai & Xin, Baogui & Santibanez Gonzalez, Ernesto D.R., 2025. "Can industrial intelligence promote net-zero development? An analysis of resource dependence," The North American Journal of Economics and Finance, Elsevier, vol. 78(C).
    4. Cantore, C. & Ferroni, F. & Le n-Ledesma, M A., 2011. "Interpreting the Hours-Technology time-varying relationship," Working papers 351, Banque de France.
    5. Frank Schorfheide, 2012. "EconomicDynamics Interviews Frank Schorfheide on DSGE Model Estimation," EconomicDynamics Newsletter, Review of Economic Dynamics, vol. 13(2), April.
    6. Yao, Fang, 2009. "Real and nominal rigidities in price setting: A bayesian analysis using aggregate data," SFB 649 Discussion Papers 2009-057, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Alejandro Justiniano & Claudio Michelacci, 2011. "The Cyclical Behavior of Equilibrium Unemployment and Vacancies in the US and Europe," NBER Working Papers 17429, National Bureau of Economic Research, Inc.
    8. Cruz Echevarría, 2015. "Income tax progressivity, growth, income inequality and welfare," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 6(1), pages 43-72, March.
    9. Enrique Martínez-García & Diego Vilán & Mark A. Wynne, 2012. "Bayesian Estimation of NOEM Models: Identification and Inference in Small Samples," Advances in Econometrics, in: DSGE Models in Macroeconomics: Estimation, Evaluation, and New Developments, pages 137-199, Emerald Group Publishing Limited.
    10. Ikeda, Daisuke, 2015. "Optimal inflation rates with the trending relative price of investment," Journal of Economic Dynamics and Control, Elsevier, vol. 56(C), pages 20-33.
    11. repec:hum:wpaper:sfb649dp2010-020 is not listed on IDEAS
    12. Jesús Rodríguez López, 2010. "Growth, fluctuations and technology in the U.S. post-war economy," Working Papers 10.01, Universidad Pablo de Olavide, Department of Economics.
    13. Alejandro Justiniano & Claudio Michelacci, 2012. "The Cyclical Behavior of Equilibrium Unemployment and Vacancies in the United States and Europe," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 8(1), pages 169-235.
    14. Yongsung Chang & Sun-Bin Kim & Frank Schorfheide, 2010. "Labor-Market Heterogeneity, Aggregation, and the Lucas Critique," RCER Working Papers 556, University of Rochester - Center for Economic Research (RCER).
    15. Justiniano, Alejandro & Preston, Bruce, 2010. "Can structural small open-economy models account for the influence of foreign disturbances?," Journal of International Economics, Elsevier, vol. 81(1), pages 61-74, May.
    16. Mennuni, Alessandro, 2014. "The Role of Curvature in the Transformation Frontier between Consumption and Investment," Discussion Paper Series In Economics And Econometrics 1407, Economics Division, School of Social Sciences, University of Southampton.
    17. Nikolay Iskrev, 2013. "On the distribution of information in the moment structure of DSGE models," 2013 Meeting Papers 339, Society for Economic Dynamics.
    18. Furlanetto, Francesco & Natvik, Gisle J. & Seneca, Martin, 2013. "Investment shocks and macroeconomic co-movement," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 208-216.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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